Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imaging - Schlereth, Maja; Stromer, Daniel; Mantri, Yash; Tsujimoto, Jason; Breininger, Katharina; Maier, Andreas; Anderson, Caesar; Garimella, Pranav S.; Jokerst, Jesse V. (Informatik aktuell, 2022)
Chronic wounds including diabetic and arterial/venous insufficiency injuries have become a major burden for healthcare systems worldwide. Demographic changes suggest that wound care will play an even bigger role in the coming decades. Predicting and monitoring response to therapy in wound care is currently largely based on visual inspection with little information on the underlying tissue. Thus, there is an urgent unmet need for innovative approaches that facilitate personalized diagnostics and treatments at the point-of-care. It has been recently shown that ultrasound imaging can monitor response to therapy in wound care, but this work required onerous manual image annotations. In this study we present initial results of a deep learning-based automatic segmentation of cross-sectional wound size in ultrasound images and identify requirements and challenges for future research on this application. Evaluation of the segmentation results underscores the potential of the proposed deep learning approach to complement non-invasive imaging with Dice scores of 0.34 (U-Net, FCN) and 0.27 (ResNet-U-Net) but also highlights the need for improving robustness further.We conclude that deep learning-supported analysis of non-invasive ultrasound images is a promising area of research to automatically extract cross-sectional wound size and depth information with potential value in monitoring response to therapy.<br
CRIS-Team Scopus Importer:2022-05-06

InSE-NET: A Perceptually Coded Audio Quality Model based on CNN - Jiang, Guanxin; Biswas, Arijit; Bergler, Christian; Maier, Andreas (Audio Engineering Society Convention 151, 2021)

Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal-guided 4D CT - Werner, Rene; Sentker, Thilo; Madesta, Frederic; Schwarz, Annette; Vornehm, Marc; Gauer, Tobias; Hofmann, Christian (Medical Physics, 2020)
Purpose Four-dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by breathing irregularity during data acquisition, which leads to projection data coverage issues for currently available commercial 4D CT protocols. It was proposed to improve projection data coverage by online respiratory signal analysis and signal-guided CT tube control, but related work was always theoretical and presented as pure in silico studies. The present work demonstrates a first CT prototype implementation along with respective phantom measurements for the recently introduced intelligent 4D CT (i4DCT) sequence scanning concept (). Methods Intelligent 4D CT was implemented on the Siemens SOMATOM go platform. Four-dimensional CT measurements were performed using the CIRS motion phantom. Motion curves were programmed to systematically vary from regular to very irregular, covering typical irregular patterns that are known to result in image artifacts using standard 4D CT imaging protocols. Corresponding measurements were performed using i4DCT and routine spiral 4D CT with similar imaging parameters (e.g., mAs setting and gantry rotation time, retrospective ten-phase reconstruction) to allow for a direct comparison of the image data. Results Following technological implementation of i4DCT on the clinical CT scanner platform, 4D CT motion artifacts were significantly reduced for all investigated levels of breathing irregularity when compared to routine spiral 4D CT scanning. Conclusions The present study confirms feasibility of fully automated respiratory signal-guided 4D CT scanning by means of a first implementation of i4DCT on a CT scanner. The measurements thereby support the conclusions of respective in silico studies and demonstrate that respiratory signal-guided 4D CT (here: i4DCT) is ready for integration into clinical CT scanners.
CRIS-Team WoS Importer:2020-09-11

Intelligibility is more than a single word: Quantification of speech intelligibility by ASR and prosody - Maier, Andreas; Haderlein, Tino; Schuster, Maria; Nkenke, Emeka; Nöth, Elmar (Text, Speech and Dialogue, 2007)
In this paper we examine the quality of the prediction of intelligibility scores of human experts. Furthermore, we investigate the differences between subjective expert raters who evaluated speech disorders of laryngectomees and children with cleft lip and palate. We use the recognition rate of a word recognizer and prosodic features to predict the intelligibility score of each individual expert. For each expert and the mean opinion of all experts we present the best features to model their scoring behavior according to the mean rank obtained during a 10-fold cross-validation. In this manner all individual speech experts were modeled with a correlation coefficient of at least r >.75. The mean opinion of all raters is predicted with a correlation of r =.90 for the laryngectomees and r =.86 for the children.

Intelligibility of Children with Cleft Lip and Palate: Evaluation by Speech Recognition Techniques - Maier, Andreas; Hacker, Christian; Nöth, Elmar; Nkenke, Emeka; Haderlein, Tino; Rosanowski, Frank; Schuster, Maria (The 18th International Conference on Pattern Recognition, 2006)

Intelligitility Assessment in Children with Cleft Lip and Palate in Italian and German - Scipioni, Marcello; Gerosa, Matteo; Giuliani, Diego; Nöth, Elmar; Maier, Andreas (Proceedings of the 10th Annual Conference of the International Speech Communication Association (Interspeech 2009), 2009)
Current research has shown that the speech intelligibility in children with cleft lip and palate (CLP) can be estimated automatically using speech recognition methods. On German CLP data high and significant correlations between human ratings and the recognition accuracy of a speech recognition system were already reported. In this paper we investigate whether the approach is also suitable for other languages. Therefore, we compare the correlations obtained on German data with the correlations on Italian data. A high and significant correlation (r=0.76; p < 0.01) was identified on the Italian data. These results do not differ significantly from the results on German data (p > 0.05).
Keywords: speech recognition; speech intelligibility; cleft lip and palate

Intensity interferometry-based 3D imaging - Wagner, Fabian; Schiffers, Florian; Willomitzer, Florian; Cossairt, Oliver; Velten, Andreas (Optics Express, 2021)
The development of single-photon counting detectors and arrays has made tremendous steps in recent years, not the least because of various new applications, e.g., LIDAR devices. In this work, a 3D imaging device based on real thermal light intensity interferometry is presented. By using gated SPAD technology, a basic 3D scene is imaged in reasonable measurement time. Compared to conventional approaches, the proposed synchronized photon counting allows the use of more light modes to enhance 3D ranging performance. Advantages like robustness to atmospheric scattering or autonomy by exploiting external light sources can make this ranging approach interesting for future applications. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
CRIS-Team WoS Importer:2021-03-19

Interactive Neural Network Robot User Investigation for Medical Image Segmentation - Amrehn, Mario; Strumia, Maddalena; Kowarschik, Markus; Maier, Andreas (Informatik aktuell, 2019)
Interactive image segmentation bears the advantage of correctional updates to the current segmentation mask when compared to fully automated systems. Especially in the field of inter-operative medical image processing of a single patient, where a high accuracy is an uncompromisable necessity, a human operator guiding a system towards an optimal segmentation result is a time-efficient constellation benefiting the patient. There are recent categories of neural networks which can incorporate human-computer interaction (HCI) data as additional input for segmentation. In this work, we simulate this HCI data during training with state-of-the-art user models, also called robot users, which aim to act similar to real users given interactive image segmentation tasks. We analyze the influence of chosen robot users, which mimic different types of users and scribble patterns, on the segmentation quality. We conclude that networks trained with robot users with the most spread out seeding patterns generalize well during inference with other robot users.
CRIS-Team Scopus Importer:2019-05-14

Interpreting acoustic features for the assessment of Alzheimer's disease using ForestNet - Pérez Toro, Paula Andrea; Rodriguez Salas, Dalia; Arias Vergara, Tomás; Klumpp, Philipp; Schuster, Maria; Nöth, Elmar; Orozco Arroyave, Juan Rafael; Maier, Andreas (Smart Health, 2022)
Nowadays, interpretable machine learning models are one of the most critical topics in the medical domain. The lack of interpretation leads to blind and unreliable models for clinicians, despite the fact that the aim is to support diagnosis through these tools. This problem has been increasing since the creation of large models such as those based on deep learning, which, despite providing good performance in prediction and classification tasks, are not transparent to human understanding. One of the increasingly prevalent clinical problems related to acoustic and linguistic disorders is Alzheimer's disease (AD), where one important challenge is to provide speech markers that help in supporting, understanding, and facilitating the diagnosis and monitoring of the disease. It motivates this study which proposes a methodology focused on analyzing acoustic features in AD and at the same time providing interpretation from the results. The proposed approach consists of using decision tree-based methods together with neural networks (ForestNet) for analyzing the classification results. Only features that can give interpretation were considered. Unweighted average recalls of up to 79% were achieved for discriminating AD patients. Then, we looked at the relevant features that provided most of the information for assessing AD, which were those related to rhythm, voiced rates, duration, and phone rates. This confirms that this kind of approach can be suitable for the discrimination of AD while maintaining a good performance.
CRIS-Team Scopus Importer:2022-11-25
Keywords: Acoustic analysis; Alzheimer's disease; ForestNets; Interpretability

Interpreting acoustic features for the assessment of Alzheimer's disease using ForestNet - Pérez Toro, Paula Andrea; Rodriguez Salas, Dalia; Arias Vergara, Tomás; Klumpp, Philipp; Schuster, Maria; Nöth, Elmar; Orozco-Arroyave, Juan Rafael; Maier, Andreas (Smart Health, 2022)
Keywords: Alzheimer's disease; ForestNets; Acoustic analysis; Interpretability

Inter-species cell detection - datasets on pulmonary hemosiderophages in equine, human and feline specimens - Marzahl, Christian; Hill, Jenny; Stayt, Jason; Bienzle, Dorothee; Welker, Lutz; Wilm, Frauke; Voigt, Joern; Aubreville, Marc; Maier, Andreas; Klopfleisch, Robert; Breininger, Katharina; Bertram, Christof A. (Scientific Data, 2022)
Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolar lavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-species P-Hem dataset, which consists of 74 cytology whole slide images (WSIs) with equine, feline and human samples. To create this high-quality and high-quantity dataset, we developed an annotation pipeline combining human expertise with deep learning and data visualisation techniques. We applied a deep learning-based object detection approach trained on 17 expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7 feline WSIs. The resulting annotations were semi-automatically screened for errors on multiple types of specialised annotation maps and finally reviewed by a trained pathologist. Our dataset contains a total of 297,383 hemosiderophages classified into five grades. It is one of the largest publicly available WSIs datasets with respect to the number of annotations, the scanned area and the number of species covered.
CRIS-Team WoS Importer:2022-06-17

Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment - Learning New Tricks from Old Dogs - Aubreville, Marc; Bertram, Christof A.; Jabari, Samir; Marzahl, Christian; Klopfleisch, Robert; Maier, Andreas (Bildverarbeitung für die Medizin 2020, 2020)
For histopathological tumor assessment, the count of mitotic figures per area is an important part of prognostication. Algorithmic approaches - such as for mitotic figure identification - have significantly improved in recent times, potentially allowing for computer-augmented or fully automatic screening systems in the future. This trend is further supported by whole slide scanning microscopes becoming available in many pathology labs and could soon become a standard imaging tool. For an application in broader fields of such algorithms, the availability of mitotic figure data sets of sufficient size for the respective tissue type and species is an important precondition, that is, however, rarely met. While algorithmic performance climbed steadily for e.g. human mammary carcinoma, thanks to several challenges held in the field, for many tumor types, data sets are not available. In this work, we assess domain transfer of mitotic figure recognition using domain adversarial training on four data sets, two from dogs and two from humans. We were able to show that domain adversarial training considerably improves accuracy when applying mitotic _gure classification learned from the canine on the human data sets (up to +12.8% in accuracy) and is thus a helpful method to transfer knowledge from existing data sets to new tissue types and species.<br
Keywords: unsupervised domain adaptation; mitotic figure; tumor diagnostic; deep neural networks

Interventional dual-energy imaging - Feasibility of rapid kV-switching on a C-arm CT system - Müller, Kerstin; Datta, Sanjit; Moiz, Ahmad; Choi, Jang-Hwan; Moore, Teri; Pung, Leland; Niebler, Christine; Gold, Garry; Maier, Andreas; Fahrig, Rebecca (Medical Physics, 2016)
Purpose: In the last years, dual-energy CT imaging has shown clinical value, thanks to its ability to differentiate materials based on their atomic number and to exploit different properties of images acquired at two different energies. C-arm CT systems are used to guide procedures in the interventional suite. Until now, there are no commercially available systems that employ dual-energy material decomposition. This paper explores the feasibility of implementing a fast kV-switching technique on a clinically available angiographic system for acquiring dual-energy C-arm CT images. Methods: As an initial proof of concept, a fast kV-switching approach was implemented on an angiographic C-arm system and the peak tube voltage during 3D rotational scans was measured. The tube voltage measurements during fast kV-switching scans were compared to corresponding measurements on kV-constant scans. Additionally, to prove stability of the requested exposure parameters, the accuracy of the delivered tube current and pulse width were also recorded and compared. In a first phantom experiment, the voxel intensity values of the individual tube voltage components of the fast kV-switching scans were compared to their corresponding kV-constant scans. The same phantom was used for a simple material decomposition between different iodine concentrations and pure water using a fast kV-switching protocol of 81 and 125 kV. In the last experiment, the same kV-switching protocol as in the phantom scan was used in an in vivo pig study to demonstrate the clinical feasibility. Results: During rapid kV-switching acquisitions, the measured tube voltage of the x-ray tube during fast switching scans has an absolute deviation of 0.23 π0.13 kV compared to the measured tube voltage produced during kV-constant acquisitions. The stability of the peak tube voltage over different scan requests was about 0.10 kV for the low and 0.46 for the high energy kV-switching scans and less than 0.1 kV for kV-constant scans, indicating slightly lower stability for kV-switching scans. The tube current resulted in a relative deviation of -1.6% for the low and 6.6% overestimation for the high tube voltage of the kV-switching scans compared to the kV-constant scans. The pulse width showed no deviation for the longer pulse width and only minor deviations (0.02 π 0.02 ms) for the shorter pulse widths compared to the kV-constant scans. The phantom experiment using different iodine concentrations showed an accurate correlation (R2< 0.99) between the extracted intensity values in the kV-switching and kV-constant reconstructed volumes, and allows for an automatic differentiation between contrast concentration down to 10% (350 mg/ml iodine) and pure water under low-noise conditions. Preliminary results of iodine and soft tissue separation showed also promising results in the first in vivo pig study. Conclusions: The feasibility of dual-energy imaging using a fast kV-switching method on an angiographic C-arm CT system was investigated. Direct measurements of beam quality in the x-ray field demonstrate the stability of the kV-switching method. Phantom and in vivo experiments showed that images did not deviate from those of corresponding kV-constant scans. All performed experiments confirmed the capability of performing fast kV-switching scans on a clinically available C-arm CT system. More complex material decomposition tasks and postprocessing steps will be part of future investigations.
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.interv
Keywords: C-arm CT; cone-beam CT (CBCT); dual-energy; rapid kV-switching

Interventional heart wall motion analysis with cardiac C-arm CT systems - Müller, Kerstin; Maier, Andreas; Zheng, Yefeng; Wang, Yang; Lauritsch, Günter; Schwemmer, Chris; Rohkohl, Christopher; Hornegger, Joachim; Fahrig, Rebecca (Physics in Medicine and Biology, 2014)
Today, quantitative analysis of three-dimensional (3D) dynamics of the left ventricle (LV) cannot be performed directly in the catheter lab using a current angiographic C-arm system, which is the workhorse imaging modality for cardiac interventions. Therefore, myocardial wall analysis is completely based on the 2D angiographic images or pre-interventional 3D/4D imaging. In this paper, we present a complete framework to study the ventricular wall motion in 4D (3D+t) directly in the catheter lab. From the acquired 2D projection images, a dynamic 3D surface model of the LV is generated, which is then used to detect ventricular dyssynchrony. Different quantitative features to evaluate LV dynamics known from other modalities (ultrasound, magnetic resonance imaging) are transferred to the C-arm CT data. We use the ejection fraction, the systolic dyssynchrony index a 3D fractional shortening and the phase to maximal contraction (φi, max) to determine an indicator of LV dyssynchrony and to discriminate regionally pathological from normal myocardium. The proposed analysis tool was evaluated on simulated phantom LV data with and without pathological wall dysfunctions. The LV data used is publicly available online at https://conrad.stanford.edu/data/heart. In addition, the presented framework was tested on eight clinical patient data sets. The first clinical results demonstrate promising performance of the proposed analysis tool and encourage the application of the presented framework to a larger study in clinical practice. © 2014 Institute of Physics and Engineering in Medicine.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.interv
Keywords: C-arm CT; cardiac dynamics; functional imaging; interventional imaging; wall motion analysis

Intraoperative brain shift compensation using a hybrid mixture model - Bayer, Siming; Ravikumar, Nishant; Strumia, Maddalena; Tong, Xiaoguang; Gao, Ying; Ostermeier, Martin; Fahrig, Rebecca; Maier, Andreas (MICCAI 2018: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, pp 116-124, Springer, 2018)
Brain deformation (or brain shift) during neurosurgical procedures such as tumor resection has a significant impact on the accuracy of neuronavigation systems. Compensating for this deformation during surgery is essential for effective guidance. In this paper, we propose a method for brain shift compensation based on registration of vessel centerlines derived from preoperative C-Arm cone beam CT (CBCT) images, to intraoperative ones. A hybrid mixture model (HdMM)-based non-rigid registration approach was formulated wherein, Student’s t and Watson distributions were combined to model positions and centerline orientations of cerebral vasculature, respectively. Following registration of the preoperative vessel centerlines to its intraoperative counterparts, B-spline interpolation was used to generate a dense deformation field and warp the preoperative image to each intraoperative image acquired. Registration accuracy was evaluated using both synthetic and clinical data. The former comprised CBCT images, acquired using a deformable anthropomorphic brain phantom. The latter meanwhile, consisted of four 3D digital subtraction angiography (DSA) images of one patient, acquired before, during and after surgical tumor resection. HdMM consistently outperformed a state-of-the-art point matching method, coherent point drift (CPD), resulting in significantly lower registration errors. For clinical data, the registration error was reduced from 3.73 mm using CPD to 1.55 mm using the proposed metho

Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study - Sievert, Matti; Stelzle, Florian; Aubreville, Marc; Mueller, Sarina K.; Eckstein, Markus; Oetter, Nicolai; Maier, Andreas; Mantsopoulos, Konstantinos; Iro, Heinrich; Goncalves, Miguel (European Archives of Oto-Rhino-Laryngology, 2021)
Purpose: This pilot study aimed to assess the feasibility of intraoperative assessment of safe margins with confocal laser endomicroscopy (CLE) during oropharyngeal squamous cell carcinoma (OPSCC) surgery. Methods: We included five consecutive patients confirmed OPSCC and planned tumor resection in September and October 2020. Healthy appearing mucosa in the marginal zone, and the tumor margin, were examined with CLE and biopsy during tumor resection. A total of 12,809 CLE frames were correlated with the gold standard of hematoxylin and eosin staining. Three head and neck surgeons and one pathologist were asked to identify carcinoma in a sample of 169 representative images, blinded to the histological results. Results: Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. CLE optical biopsy of OPSCC demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, PPV, and NPV of 86%, 90%, 79%, 88%, and 82%, respectively, with inter-rater reliability and κ-value of 0.60. Conclusion: CLE can be easily integrated into the intraoperative setting, generate real-time, in-vivo microscopic images of the oropharynx for evaluation and demarcation of cancer. It can eventually contribute to a less radical approach by enabling a more precise evaluation of the cancer margin.
CRIS-Team Scopus Importer:2021-03-05
Keywords: Confocal laser endomicroscopy; Head and neck malignancies; Oropharyngeal squamous cell carcinoma; Safe surgical margins

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery - Bayer, Siming; Maier, Andreas; Fahrig, Rebecca; Ostermeier, Martin (International Journal of Biomedical Imaging, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.intrao

Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair - Breininger, Katharina; Albarqouni, Shadi; Kurzendorfer, Tanja; Pfister, Marcus; Kowarschik, Markus; Maier, Andreas (CARS 2018—Computer Assisted Radiology and Surgery Proceedings of the 32nd International Congress and Exhibition Berlin, Germany, June 20–23, 2018, 2018)
Purpose: Fusion of preoperative data with intraoperative X-ray images has proven the potential to reduce radiation exposure and contrast agent, especially for complex endovascular aortic repair (EVAR). Due to patient movement and introduced devices that deform the vasculature, the fusion can become inaccurate. This is usually detected by comparing the preoperative information with the contrasted vessel. To avoid repeated use of iodine, comparison with an implanted stent can be used to adjust the fusion. However, detecting the stent automatically without the use of contrast is challenging as only thin stent wires are visible. Method: We propose a fast, learning-based method to segment aortic stents in single uncontrasted X-ray images. To this end, we employ a fully convolutional network with residual units. Additionally, we investigate whether incorporation of prior knowledge improves the segmentation. Results: We use 36 X-ray images acquired during EVAR for training and evaluate the segmentation on 27 additional images. We achieve a Dice coefficient of 0.933 (AUC 0.996) when using X-ray alone, and 0.918 (AUC 0.993) and 0.888 (AUC 0.99) when adding the preoperative model, and information about the expected wire width, respectively. Conclusion: The proposed method is fully automatic, fast and segments aortic stent grafts in fluoroscopic images with high accuracy. The quality and performance of the segmentation will allow for an intraoperative comparison with the preoperative information to assess the accuracy of the fusion.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.intrao_2
Keywords: Aortic stents; Convolutional neural network; Deep learning; EVAR; Fluoroscopy; Segmentation

Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair - Breininger, Katharina; Albarqouni, Shadi; Kurzendorfer, Tanja; Pfister, Marcus; Kowarschik, Markus; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2018)
Purpose: Fusion of preoperative data with intraoperative X-ray images has proven the potential to reduce radiation exposure and contrast agent, especially for complex endovascular aortic repair (EVAR). Due to patient movement and introduced devices that deform the vasculature, the fusion can become inaccurate. This is usually detected by comparing the preoperative information with the contrasted vessel. To avoid repeated use of iodine, comparison with an implanted stent can be used to adjust the fusion. However, detecting the stent automatically without the use of contrast is challenging as only thin stent wires are visible. Method: We propose a fast, learning-based method to segment aortic stents in single uncontrasted X-ray images. To this end, we employ a fully convolutional network with residual units. Additionally, we investigate whether incorporation of prior knowledge improves the segmentation. Results: We use 36 X-ray images acquired during EVAR for training and evaluate the segmentation on 27 additional images. We achieve a Dice coefficient of 0.933 (AUC 0.996) when using X-ray alone, and 0.918 (AUC 0.993) and 0.888 (AUC 0.99) when adding the preoperative model, and information about the expected wire width, respectively. Conclusion: The proposed method is fully automatic, fast and segments aortic stent grafts in fluoroscopic images with high accuracy. The quality and performance of the segmentation will allow for an intraoperative comparison with the preoperative information to assess the accuracy of the fusion.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.intrao
Keywords: Aortic stents; EVAR; Fluoroscopy; Convolutional neural network; Deep learning; Segmentation

Intrinsic Noise Improves Speech Recognition in a Computational Model of the Auditory Pathway - Schilling, Achim; Gerum, Richard; Metzner, Claus; Maier, Andreas; Krauß, Patrick (Frontiers in Neuroscience, 2022)
Noise is generally considered to harm information processing performance. However, in the context of stochastic resonance, noise has been shown to improve signal detection of weak sub- threshold signals, and it has been proposed that the brain might actively exploit this phenomenon. Especially within the auditory system, recent studies suggest that intrinsic noise plays a key role in signal processing and might even correspond to increased spontaneous neuronal firing rates observed in early processing stages of the auditory brain stem and cortex after hearing loss. Here we present a computational model of the auditory pathway based on a deep neural network, trained on speech recognition. We simulate different levels of hearing loss and investigate the effect of intrinsic noise. Remarkably, speech recognition after hearing loss actually improves with additional intrinsic noise. This surprising result indicates that intrinsic noise might not only play a crucial role in human auditory processing, but might even be beneficial for contemporary machine learning approaches.
CRIS-Team Scopus Importer:2022-07-15
Keywords: auditory perception; deep artificial neural networks; dorsal cochlear nucleus; hearing loss; speech processing; stochastic resonance; tinnitus mechanisms; Zwicker tone

Investigating Subtle Structural Changes in Eyes with Age-Related Macular Degeneration: A Pilot Study Using Ultrahigh-Resolution Optical Coherence Tomography - Chen, Siyu; Abu-Qmar, Omar; Moult, Eric; Lee, Byungkun; Ploner, Stefan; Maier, Andreas; Waheed, Nadia K.; Fujimoto, James G. (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2020)
CRIS-Team WoS Importer:2020-09-11

Investigating the Effect of Using Synthetic and Semi-synthetic Images for Historical Document Font Classification - Nikolaidou, Konstantina; Upadhyay, Richa; Seuret, Mathias; Liwicki, Marcus (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
This paper studies the effect of using various data augmentation by synthetization approaches on historical image data, particularly for font classification. Historical document image datasets often lack the appropriate size to train and evaluate deep learning models, motivating data augmentation and synthetic document generation techniques for creating additional data. This work explores the effect of various semi-synthetic and synthetic historical document images, some of which appear as recent trends and others not published yet, on a font classification task. We use 10K patch samples as baseline dataset, derived from the dataset of Early Printed Books with Multiple Font Groups, and increase its size using DocCreator software and Generative Adversarial Networks (GAN). Furthermore, we fine-tune different pre-trained Convolutional Neural Network (CNN) classifiers as a baseline using the original dataset and then compare the performance with the additional semi-synthetic and synthetic images. We further evaluate the performance using additional real samples from the original dataset in the training process. DocCreator, and the additional real samples improve the performance giving the best results. Finally, for the best-performing architecture, we explore different sizes of training sets and examine how the gradual addition of data affects the performance.
CRIS-Team Scopus Importer:2022-06-10
Keywords: Convolutional neural networks; Document Image Analysis; Font classification; Generative Adversarial Networks; Historical document images; Performance evaluation; Synthetic image generation

Investigation of Single Photon Emission Computed Tomography Acquired on Helical Trajectories - Oppelt, Maximilian; Sanders, James Chester; Maier, Andreas (Bildverarbeitung für die Medizin, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.invest

Investigation of the effect of small but frequently occurring patient movements onto 3D activity quantitation in Siemens xSPECT Quant reconstruction with integrated motion correction - Reymann, Maximilian; et al. (EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2021)

Investigations on Self-supervised Learning for Script-, Font-type, and Location Classification on Historical Documents - Zenk, Johan; Kordon, Florian Johannes; Mayr, Martin; Seuret, Mathias; Christlein, Vincent (ACM International Conference Proceeding Series, 2023)
In the context of automated classification of historical documents, we investigate three contemporary self-supervised learning (SSL) techniques (SimSiam, Dino, and VICReg) for the pre-training of three different document analysis tasks, namely script-type, font-type, and location classification. Our study draws samples from multiple datasets that contain images of manuscripts, prints, charters, and letters. The representations derived via pre-text training are taken as inputs for k-NN classification and a parametric linear classifier. The latter is placed atop the pre-trained backbones to enable fine-tuning of the entire network to further improve the classification by exploiting task-specific label data. The network’s final performance is assessed via independent test sets obtained from the ICDAR2021 Competition on Historical Document Classification. We empirically show that representations learned with SSL are significantly better suited for subsequent document classification than features generated by commonly used transfer learning on ImageNet.
CRIS-Team Scopus Importer:2023-09-08
Keywords: classification; document analysis; self-supervised learning

In Vivo 3D Measurement of Time-dependent Human Knee Joint Compression and Cartilage Strain During Static Weight-Bearing - Choi, Jang-Hwan; McWalter, Emily; Datta, Sanjit; Müller, Kerstin; Maier, Andreas; Gold, Garry; Levenstone, Marc; Fahrig, Rebecca (ORS 2016 Annual Meeting, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.invivo

Is crowd-algorithm collaboration an advanced alternative to crowd-sourcing on cytology slides? - Marzahl, Christian; Aubreville, Marc; Bertram, Christof A.; Gerlach, Stefan; Maier, Jennifer; Voigt, Jörn; Hill, Jenny; Klopfleisch, Robert; Maier, Andreas (Informatik aktuell, 2020)
Modern, state-of-the-art deep learning approaches yield human like performance in numerous object detection and classification tasks. The foundation for their success is the availability of training datasets of substantially high quantity, which are expensive to create, especially in the field of medical imaging. Crowdsourcing has been applied to create large datasets for a broad range of disciplines. This study aims to explore the challenges and opportunities of crowd-algorithm collaboration for the object detection task of grading cytology whole slide images. We compared the classical crowdsourcing performance of twenty participants with their results from crowd-algorithm collaboration. All participants performed both modes in random order on the same twenty images. Additionally, we introduced artificial systematic flaws into the precomputed annotations to estimate a bias towards accepting precomputed annotations. We gathered 9524 annotations on 800 images from twenty participants organised into four groups in concordance to their level of expertise with cytology. The crowd-algorithm mode improved on average the participants’ classification accuracy by 7%, the mean average precision by 8% and the inter-observer Fleiss’ kappa score by 20%, and reduced the time spent by 31%. However, two thirds of the artificially modified false labels were not recognised as such by the contributors. This study shows that crowd-algorithm collaboration is a promising new approach to generate large datasets when it is ensured that a carefully designed setup eliminates potential biases.
CRIS-Team Scopus Importer:2020-04-21

Is Multitask Learning Always Better? - Mattick, Alexander; Mayr, Martin; Maier, Andreas; Christlein, Vincent (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
Multitask learning has been a common technique for improving representations learned by artificial neural networks for decades. However, the actual effects and trade-offs are not much explored, especially in the context of document analysis. We demonstrate a simple and realistic scenario on real-world datasets that produces noticeably inferior results in a multitask learning setting than in a single-task setting. We hypothesize that slight data-manifold and task semantic shifts are sufficient to lead to adversarial competition of tasks inside networks and demonstrate this experimentally in two different multitask learning formulations.
CRIS-Team Scopus Importer:2022-06-10
Keywords: Deep learning; Document analysis; Document classification; Multitask learning

Isocenter Determination from Projection Matrices of a C-Arm CBCT - Amri, Ahmed; Bier, Bastian; Maier, Jennifer; Maier, Andreas (Informatik aktuell, 2019)
An accurate position of the isocenter of a cone-beam CT trajectory is mandatory for accurate image reconstruction. For analytical backprojection algorithms, it is assumed that the X-ray source moves on a perfectly circular trajectory, which is not true for most practical clinical trajectories due to mechanical instabilities. Besides, the flexibility of novel robotic C-arm systems enables new trajectories where the computation of the isocenter might not be straight forward. An inaccurate isocenter position directly affects the computation of the redundancy weights and consequently affects the reconstructions immediately. In this work, we compare different methods for computing the isocenter of a non-ideal circular scan trajectory and evaluate their robustness in the presence of noise. The best results were achieved using a method based on a least-square-based fit. Furthermore, we show that an inaccurate isocenter computation can lead to artifacts in the reconstruction result. Therefore, this work highlights the importance of an accurate isocenter computation with the background of novel upcoming clinical trajectories.
CRIS-Team Scopus Importer:2019-05-14

Isotropic 3-D CINE Imaging with Sub-2mm Resolution in a Single Breath-Hold - Wetzl, Jens; Schmidt, Michaela; Zenge, Michael O.; Lugauer, Felix; Lazar, Laszlo; Nadar, Mariappan; Maier, Andreas; Hornegger, Joachim; Forman, Christoph (Proceedings of the 23rd Annual Meeting of the ISMRM (ISMRM 2015), 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.isotro

Iterative Cross-Scanner Registration for Whole Slide Images - Theelke, Luisa; Wilm, Frauke; Marzahl, Christian; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas; Aubreville, Marc; Breininger, Katharina (2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021), 2021)
The successful registration of digitized microscopic images is required for many applications in digital pathology. In particular, the registration of specimens scanned by different slide scanning systems may be beneficial to transfer expert annotations from one image domain to another and thereby reduce labeling effort. We present an iterative approach to register microscopic specimens digitized with multiple scanning systems, aiming to compute an optimal global transformation for the images at highest resolution. For this purpose, an initial registration based on a down-scaled version of the images is followed by a patch-based iterative update scheme. We make use of the hierarchical structure of digitized whole slide images to gradually approximate the optimal transformation. By using kernel density estimation to weight local transformation estimates, the influence of registration errors can be further mitigated. We validate our method on five histologic and five cytologic samples, each scanned with four different scanning systems. Furthermore, we perform first experiments on samples stained with different stain combinations. Our experiments demonstrate the potential of the proposed method for a variety of datasets and application fields.<br
CRIS-Team WoS Importer:2022-02-25

Iterative CT Reconstruction Using Curvelet-Based Regularization - Wu, Haibo; Maier, Andreas; Hornegger, Joachim (Proceedings des Workshops Bildverarbeitung für die Medizin 2013, 2013)

JBFnet: Low Dose CT-denoising by Trainable Joint Bilateral Filtering - Patwari, Mayank; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

JBFnet - Low Dose CT Denoising by Trainable Joint Bilateral Filtering - Patwari, Mayank; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Surgery (MICCAI), 2020)
Deep neural networks have shown great success in low dose CT denoising. However, most of these deep neural networks have several hundred thousand trainable parameters. This, combined with the inherent non-linearity of the neural network, makes the deep neural network difficult to understand with low accountability. In this study we introduce JBFnet, a neural network for low dose CT denoising. The architecture of JBFnet implements iterative bilateral filtering. The filter functions of the Joint Bilateral Filter (JBF) are learned via shallow convolutional networks. The guidance image is estimated by a deep neural network. JBFnet is split into four filtering blocks, each of which performs Joint Bilateral Filtering. Each JBF block consists of 112 trainable parameters, making the noise removal process comprehendable. The Noise Map (NM) is added after filtering to preserve high level features. We train JBFnet with the data from the body scans of 10 patients, and test it on the AAPM low dose CT Grand Challenge dataset. We compare JBFnet with state-of-the-art deep learning networks. JBFnet outperforms CPCE3D, GAN and deep GFnet on the test dataset in terms of noise removal while preserving structures. We conduct several ablation studies to test the performance of our network architecture and training method. Our current setup achieves the best performance, while still maintaining behavioural accountability.<br
Keywords: Low Dose CT Denoising; Joint Bilateral Filtering; Precision Learning; Convolutional Neural Networks

Joint Calibration and Motion Estimation in Weight-Bearing Cone-Beam CT of the Knee Joint using Fiducial Markers - Syben-Leisner, Christopher; Bier, Bastian; Berger, Martin; Aichert, André; Fahrig, Rebecca; Gold, Garry; Levenston, Marc; Maier, Andreas (Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.jointc_3
Keywords: C-arm CBCT; Image Reconstruction; Knee-Joint Imaging; Motion Estimation; Self-Calibration

Joint Estimation of Cardiac Motion and T1* Maps for Magnetic Resonance Late Gadolinium Enhancement Imaging - Wetzl, Jens; Stalder, Aurélien F.; Schmidt, Michaela; Akgök, Yigit H.; Tillmanns, Christoph; Lugauer, Felix; Forman, Christoph; Hornegger, Joachim; Maier, Andreas (Lecture Notes in Computer Science, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.jointe

Joint Super-Resolution and Rectification for Solar Cell Inspection - Hoffmann, Mathis; Köhler, Thomas; Doll, Bernd; Schebesch, Frank; Talkenberg, Florian; Peters, Ian Marius; Brabec, Christoph; Maier, Andreas; Christlein, Vincent (IEEE Journal of Photovoltaics, 2021)
Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants. Since a single measurement of fast CMOS sensors is limited in spatial resolution and often not sufficient to reliably detect small defects, we apply multi-frame super-resolution (MFSR) to a sequence of low resolution measurements. In addition, the rectification and removal of lens distortion simplifies subsequent analysis. Therefore, we propose to fuse this pre-processing with standard MFSR algorithms. This is advantageous, because we omit a separate processing step, the motion estimation becomes more stable and the spacing of high-resolution (HR) pixels on the rectified module image becomes uniform w.r.t. the module plane, regardless of perspective distortion. We present a comprehensive user study showing that MFSR is beneficial for defect recognition by human experts and that the proposed method performs better than the state of the art. Furthermore, we apply automated crack segmentation and show that the proposed method performs 3x better than bicubic upsampling and 2x better than the state of the art for automated inspection.

Keeping the Pace: Heart Rate Informed 3-D Motion Detection for Adaptive Temporal Smoothing - Taubmann, Oliver; Lauritsch, Günter; Maier, Andreas; Fahrig, Rebecca; Hornegger, Joachim (Proceedings of the 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.keepin

“Keep it simple, scholar”: an experimental analysis of few-parameter segmentation networks for retinal vessels in fundus imaging - Fu, Weilin; Breininger, Katharina; Schaffert, Roman; Pan, Zhaoya; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2021)
Purpose: With the recent development of deep learning technologies, various neural networks have been proposed for fundus retinal vessel segmentation. Among them, the U-Net is regarded as one of the most successful architectures. In this work, we start with simplification of the U-Net, and explore the performance of few-parameter networks on this task. Methods: We firstly modify the model with popular functional blocks and additional resolution levels, then we switch to exploring the limits for compression of the network architecture. Experiments are designed to simplify the network structure, decrease the number of trainable parameters, and reduce the amount of training data. Performance evaluation is carried out on four public databases, namely DRIVE, STARE, HRF and CHASE_DB1. In addition, the generalization ability of the few-parameter networks are compared against the state-of-the-art segmentation network. Results: We demonstrate that the additive variants do not significantly improve the segmentation performance. The performance of the models are not severely harmed unless they are harshly degenerated: one level, or one filter in the input convolutional layer, or trained with one image. We also demonstrate that few-parameter networks have strong generalization ability. Conclusion: It is counter-intuitive that the U-Net produces reasonably good segmentation predictions until reaching the mentioned limits. Our work has two main contributions. On the one hand, the importance of different elements of the U-Net is evaluated, and the minimal U-Net which is capable of the task is presented. On the other hand, our work demonstrates that retinal vessel segmentation can be tackled by surprisingly simple configurations of U-Net reaching almost state-of-the-art performance. We also show that the simple configurations have better generalization ability than state-of-the-art models with high model complexity. These observations seem to be in contradiction to the current trend of continued increase in model complexity and capacity for the task under consideration.
CRIS-Team Scopus Importer:2021-05-21
Keywords: Computation cost; Fundus image; U-Net; Vessel segmentation

Kinect-Based Correction of Overexposure Artifacts in Knee Imaging with C-Arm CT Systems - Rausch, Johannes; Maier, Andreas; Fahrig, Rebecca; Choi, Jang-Hwan; Hinshaw, Waldo; Schebesch, Frank; Haase, Sven; Wasza, Jakob; Hornegger, Joachim; Riess, Christian (International Journal of Biomedical Imaging, 2016)

Known operator learning and hybrid machine learning in medical imaging - A review of the past, the present, and the future - Maier, Andreas; Köstler, Harald; Heisig, Marco; Krauss, Patrick; Yang, Seung Hee (Progress in Biomedical Engineering, 2022)
In this article, we perform a review of the state-of-the-art of hybrid machine learning in medical imaging. We start with a short summary of the general developments of the past in machine learning and how general and specialized approaches have been in competition in the past decades. A particular focus will be the theoretical and experimental evidence pro and contra hybrid modelling. Next, we inspect several new developments regarding hybrid machine learning with a particular focus on so-called known operator learning and how hybrid approaches gain more and more momentum across essentially all applications in medical imaging and medical image analysis. As we will point out by numerous examples, hybrid models are taking over in image reconstruction and analysis. Even domains such as physical simulation and scanner and acquisition design are being addressed using machine learning grey box modelling approaches. Towards the end of the article, we will investigate a few future directions and point out relevant areas in which hybrid modelling, meta learning, and other domains will likely be able to drive the state-of-the-art ahead.
CRIS-Team Scopus Importer:2022-04-15
Keywords: grey-box models; hybrid machine learning; known operator learning; machine learning; medical image processing; review

Known Operator Learning enables Constrained Projection Geometry Conversion: Parallel to Cone-beam for Hybrid MR/X-ray Imaging - Syben-Leisner, Christopher; Stimpel, Bernhard; Roser, Philipp; Dörfler, Arnd; Maier, Andreas (IEEE Transactions on Medical Imaging, 2020)
X-ray imaging is a wide-spread real-time imaging technique. Magnetic Resonance Imaging (MRI) offers a multitude of contrasts that offer improved guidance to interventionalists. As such simultaneous real-time acquisition and overlay would be highly favorable for image-guided interventions, e.g., in stroke therapy. One major obstacle in this setting is the fundamentally different acquisition geometry. MRI k-space sampling is associated with parallel projection geometry, while the Xray acquisition results in perspective distorted projections. The classical rebinning methods to overcome this limitation inherently suffers from a loss of resolution. To counter this problem, we present a novel rebinning algorithm for parallel to cone-beam conversion. We derive a rebinning formula that is then used to find an appropriate deep neural network architecture. Following the known operator learning paradigm, the novel algorithm is mapped to a neural network with differentiable projection operators enabling data-driven learning of the remaining unknown operators. The evaluation aims in two directions: First, we give a profound analysis of the different hypotheses to the unknown operator and investigate the influence of numerical training data. Second, we evaluate the performance of the proposed method against the classical rebinning approach. We demonstrate that the derived network achieves better results than the baseline method and that such operators can be trained with simulated data without losing their generality making them applicable to real data without the need for retraining or transfer learning.<br
Keywords: Detectors; Magnetic resonance imaging; Geometry; X-ray imaging; Tomography; Image reconstruction

Known Operator Learning for a Hybrid Magnetic Resonance/X-ray Imaging Acquisition Scheme - Syben, Christopher (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2021)
With today’s technology, various non-invasive imaging methods provide detailed insight into the patient’s anatomy and support the physician during surgery. Depending on the signal type of each modality, the information collected is limited to narrow parts of the overall available information. A combination of several modalities makes it possible to increase the amount of information simultaneously made available to the physician. Especially a possible combination of the two most frequently used modalities, namely magnetic resonance imaging (MRI) and X-ray/computed tomography (CT) is of particular interest. Apart from the physical challenges, such a combination also poses a challenge to the acquisition scheme for a meaningful simultaneous acquisition with both modalities. This is especially true for the interventional environment, which implies additional requirements such as the real-time capability of the acquisition scheme. Such a combination, comes along with limitations for the signal recording schemes, up to the degree that an analytically correct solution can no longer be derived. Recently, there have been different attempts to overcome such limitations with machine learning. However, the solutions found are only partially comprehensible, and signal authenticity cannot be guaranteed. With the integration of prior knowledge about signal acquisition processes, such models can also be used in the medical field. In this thesis, we outline an acquisition scheme for a novel hybrid magnetic resonance (MR)/X-ray imaging system for the interventional environment and investigate the realization of parts of the scheme. Further, we investigate the concept of known operator learning, derive an implementation for operators in CT, and utilize the concept to enable the aforementioned hybrid MR/X-ray acquisition scheme. First, we investigate the possibilities and limitations for incorporating prior knowledge about the signal processing chain into machine learning algorithms. Using the universal approximation theorem (UAT), the benefits of mixing prior knowledge with neural networks are theoretically analyzed. Such a learning pipeline enriched with known operators allows the reduction of trainable parameters, and constrains the pipeline such that signal authenticity can be met, while the approximative power of deep learning (DL) can be utilized. In the course of the thesis, the concept is implemented for the CT reconstruction and evaluated from a methodological and algorithmic point of view. The results show that CT operators can be integrated into neural networks allowing gradient flow through the whole pipeline. The experiments suggest that such mixed pipelines can be trained only with numerical data if designed specifically for the problem. Furthermore, an open-source software framework called PYRO-NN is developed to make these benefits accessible to a broader community. Second, we develop a novel hybrid MR/X-ray acquisition scheme for image-guided interventions. The scheme acknowledges the high contrast diversity of MRI and the benefits of fast, high-resolution X-ray imaging. The information provided by both modalities is captured in different domains, making a subsequent exploitation of the complementary signals difficult, and is identified as a major obstacle to fully benefit from the simultaneous hybrid acquisition. The proposed scheme results in multiple orthographic MR projections and, in one perspectively distorted X-ray projection per frame. In the course of the thesis, we first develop a novel tomographic conversion scheme to overcome the limitations of classical geometrical rebinning. The tomographic rebinning is derived and performed utilizing the known operator learning scheme to learn an efficient convolution-based algorithm for the conversion. The novel concept is first designed and comprehensively evaluated on the 1-D/2-D case against the baseline method. Subsequently, the tomographic rebinning concept is developed for the clinically relevant 2-D/3-D case. In the course of the experiments, we can show that the efficient convolution-based algorithm can be learned with purely numeric training data. The presented method overcomes the limitation of the baseline method and provides sharper and more distinct projection than the baseline method. Hence, the learned tomographic rebinning algorithm is promising for the proposed hybrid MR/X-ray acquisition scheme, and the underlying known operator learning concept encourages further integration of the acquisition and contrast parameters into the trainable pipeline. In general, the benefit of incorporating domain knowledge into neural networks is highly promising and is possible for various other tasks even beyond medical imaging.<br
Keywords: CT; Machine Learning; Known Operator Learaning; Hybrid Imaging

Kritischer Katalog der Luther-Bildnisse (1519-1530) - Schubert, Anselm; Hess, Daniel; Heydenreich, Gunnar; Mack, Oliver; Maier, Andreas; Görres, Daniel; Hänsch, Anna-Amalie; Klinke, Thomas; Ottweiler, Wibke; Sindel, Aline; Christlein, Vincent (Gütersloher Verlagshaus, 2023)

Künstliche Intelligenz als Marktteilnehmer – Technische Möglichkeiten - Maier, Andreas; Bayer, Siming (Marktermöglichung durch Recht im Digitalen Zeitalter, 2023)
Kartellgesetze (auch als Wettbewerbsgesetze bezeichnet) werden entwickelt, um einen starken Wettbewerb zu fördern und haben den Zweck, Verbraucher vor räuberischen Geschäftspraktiken zu schützen. Oberste Ziele des Kartellrechts sind die Gewährleistung der Funktionsweise der Märkte sowie die Gewährleistung eines fairen Wettbewerbs. Ein prominentes Beispiel für einen Verstoß gegen das Kartellrecht ist illegale Preisabsprache. Definitionsgemäß handelt es sich um eine Vereinbarung zwischen Wettbewerbern, die Preise oder andere Wettbewerbsbedingungen festlegt und somit gegen den Grundsatz des Preisbildungsmechanismus durch freie Marktkräfte verstößt. Ein typisches Merkmal illegaler Preisabsprachen ist die nachweisbare Kommunikation (schriftlich oder mündlich) zwischen menschlichen Marktteilnehmern. Im Zeitalter der künstlichen Intelligenz und des E-Commerce steht die Aufdeckung dieser illegalen Praxis jedoch vor neuen Herausforderungen, da Verhaltensweisen, die gegen Kartellrechte verstoßen, wie zum Beispiel der Preisbildungsmechanismus, teil- oder vollautomatisiert sind. Außerdem sind die Marktteilnehmer nicht unbedingt Menschen. Mit anderen Worten, es stehen neue technologische Möglichkeiten zur Verfügung, um illegale Preispolitik und Geschäftspraktiken zu verschleiern. In diesem Kurzbeitrag möchten wir uns genau dieser Fragestellung widmen, nämlich welche grundlegenden technischen Möglichkeiten bringt die künstliche Intelligenz? Wie sieht die Kommunikation auf den elektronischen Märkten aus? Und zu guter Letzt, welche Bedeutung und Konsequenzen hat der Einsatz von der künstlichen Intelligenz auf elektronische Märkte für die Kartellgesetz
Keywords: Antitrust Law; AI; E-Commerce

Labeling, cutting, grouping: An efficient text line segmentation method for medieval manuscripts - Alberti, Michele; Vogtlin, Lars; Pondenkandath, Vinaychandran; Seuret, Mathias; Ingold, Rolf; Liwicki, Marcus (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 2019)
This paper introduces a new way for text-line extraction by integrating deep-learning based pre-classification and state-of-the-art segmentation methods. Text-line extraction in complex handwritten documents poses a significant challenge, even to the most modern computer vision algorithms. Historical manuscripts are a particularly hard class of documents as they present several forms of noise, such as degradation, bleed-through, interlinear glosses, and elaborated scripts. In this work, we propose a novel method which uses semantic segmentation at pixel level as intermediate task, followed by a text-line extraction step. We measured the performance of our method on a recent dataset of challenging medieval manuscripts and surpassed state-of-the-art results by reducing the error by 80.7%. Furthermore, we demonstrate the effectiveness of our approach on various other datasets written in different scripts. Hence, our contribution is two-fold. First, we demonstrate that semantic pixel segmentation can be used as strong denoising pre-processing step before performing text line extraction. Second, we introduce a novel, simple and robust algorithm that leverages the high-quality semantic segmentation to achieve a text-line extraction performance of 99.42% line IU on a challenging dataset.
CRIS-Team Scopus Importer:2021-04-20
Keywords: Document image analysis; Neural networks; Textline segmentation

LACOPE: Latency-Constrained Pitch Estimation for Speech Enhancement - Schröter, Hendrik; Rosenkranz, Tobias; Escalante Banuelos, Alberto; Maier, Andreas (Proc. Interspeech 2021, 2021)
Fundamental frequency (f0) estimation, also known as pitch tracking, has been a long-standing research topic in the speech and signal processing community. Many pitch estimation algorithms, however, fail in noisy conditions or introduce large delays due to their frame size or Viterbi decoding. In this study, we propose a deep learning-based pitch estimation algorithm, LACOPE, which was trained in a joint pitch estimation and speech enhancement framework. In contrast to previous work, this algorithm allows for a configurable latency down to an algorithmic delay of 0. This is achieved by exploiting the smoothness properties of the pitch trajectory. That is, a recurrent neural network compensates delay introduced by the feature computation by predicting the pitch for a desired point, allowing a trade-off between pitch accuracy and latency. We integrate the pitch estimation in a speech enhancement framework for hearing aids. For this application, we allow a delay on the analysis side of approx. 5 ms. The pitch estimate is then used for constructing a comb filter in frequency domain as post-processing step to remove intra-harmonic noise. Our pitch estimation performance is on par with SOTA algorithms like PYIN or CREPE for spoken speech in all noise conditions while introducing minimal latency.
Keywords: pitch estimation; speech enhancement; convolutional recurrent neural network

Large language models streamline automated machine learning for clinical studies - Tayebi Arasteh, Soroosh; Han, Tianyu; Lotfinia, Mahshad; Kuhl, Christiane; Jakob Nikolas, Kather; Truhn, Daniel; Nebelung, Sven (Nature Communications, 2024)
A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical data analysis. We investigated the potential of the ChatGPT Advanced Data Analysis (ADA), an extension of GPT-4, to bridge this gap and perform ML analyses efficiently. Real-world clinical datasets and study details from large trials across various medical specialties were presented to ChatGPT ADA without specific guidance. ChatGPT ADA autonomously developed state-of-the-art ML models based on the original study’s training data to predict clinical outcomes such as cancer development, cancer progression, disease complications, or biomarkers such as pathogenic gene sequences. Following the re-implementation and optimization of the published models, the head-to-head comparison of the ChatGPT ADA-crafted ML models and their respective manually crafted counterparts revealed no significant differences in traditional performance metrics (p ≥ 0.072). Strikingly, the ChatGPT ADA-crafted ML models often outperformed their counterparts. In conclusion, ChatGPT ADA offers a promising avenue to democratize ML in medicine by simplifying complex data analyses, yet should enhance, not replace, specialized training and resources, to promote broader applications in medical research and practice.

Latent Shape Constraint for Anatomical Landmark Detection on Spine Radiographs - Kordon, Florian Johannes; Maier, Andreas; Kunze, Holger (Bildverarbeitung für die Medizin 2021, 2021)
Vertebral corner points are frequently used landmarks for a vast variety of orthopedic and trauma surgical applications. Algorithmic approaches that are designed to automatically detect them on 2D radiographs have to cope with varying image contrast, high noise levels, and superimposed soft tissue. To enforce an anatomically correct landmark configuration in presence of these limitations, this study investigates a shape constraint technique based on data-driven encodings of the spine geometry. A contractive PointNet autoencoder is used to map numerical landmark coordinate representations onto a low-dimensional shape manifold. A distance norm between prediction and ground truth encodings then serves as an additional loss term during optimization. The method is compared and evaluated on the SpineWeb16 dataset. Small improvements can be observed, recommending further analysis of the encoding design and composite cost function.<br
Keywords: Autoencoder; Shape constraint; X-ray Image Processing

Layered X-ray Motion Estimation using Primal-Dual Optimization - Mehmood, Ehsan; Fischer, Peter; Pohl, Thomas; Horz, Tim; Maier, Andreas (Informatik aktuell, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.layere

Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations - Thies, Mareike; Wagner, Fabian; Gu, Mingxuan; Folle, Lukas; Felsner, Lina; Maier, Andreas (Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 1230409 (2022), 2022)
Learned iterative reconstruction algorithms for inverse problems offer the flexibility to combine analytical knowledge about the problem with modules learned from data. This way, they achieve high reconstruction performance while ensuring consistency with the measured data. In computed tomography, extending such approaches from 2D fan-beam to 3D cone-beam data is challenging due to the prohibitively high GPU memory that would be needed to train such models. This paper proposes to use neural ordinary differential equations to solve the reconstruction problem in a residual formulation via numerical integration. For training, there is no need to backpropagate through several unrolled network blocks nor through the internals of the solver. Instead, the gradients are obtained very memory-efficiently in the neural ODE setting allowing for training on a single consumer graphics card. The method is able to reduce the root mean squared error by over 30% compared to the best performing classical iterative reconstruction algorithm and produces high quality cone-beam reconstructions even in a sparse view scenario.
Keywords: Inverse problems; computed tomography; iterative reconstruction; known operators

Learning an Attention Model for Robust 2-D/3-D Registration Using Point-To-Plane Correspondences - Schaffert, Roman; Wang, Jian; Fischer, Peter; Borsdorf, Anja; Maier, Andreas (IEEE Transactions on Medical Imaging, 2020)
Minimally invasive procedures rely on image guidance for navigation at the operation site to avoid large surgical incisions. X-ray images are often used for guidance, but important structures may be not well visible. These structures can be overlaid from pre-operative 3-D images and accurate alignment can be established using 2-D/3-D registration. Registration based on the point-to-plane correspondence model was recently proposed and shown to achieve state-of-the-art performance. However, registration may still fail in challenging cases due to a large portion of outliers. In this paper, we describe a learning-based correspondence weighting scheme to improve the registration performance. By learning an attention model, inlier correspondences get higher attention in the motion estimation while the outlier correspondences are suppressed. Instead of using per-correspondence labels, our objective function allows to train the model directly by minimizing the registration error. We demonstrate a highly increased robustness, e.g. increasing the success rate from 84.9% to 97.0% for spine registration. In contrast to previously proposed learning-based methods, we also achieve a high accuracy of around 0.5mm mean re-projection distance. In addition, our method requires a relatively small amount of training data, is able to learn from simulated data, and generalizes to images with additional structures which are not present during training. Furthermore, a single model can be trained for both, different views and different anatomical structures.
CRIS-Team WoS Importer:2020-10-16

Learning-based correspondence estimation for 2-D/3-D registration - Schaffert, Roman; Weiß, Markus; Wang, Jian; Borsdorf, Anja; Maier, Andreas (Informatik aktuell, 2020)
In many minimally invasive procedures, image guidance using a C-arm system is utilized. To enhance the guidance, information from pre-operative 3-D images can be overlaid on top of the 2-D fluoroscopy and 2-D/3-D image registration techniques are used to ensure an accurate overlay. Despite decades of research, achieving a highly reliable registration remains challenging. In this paper, we propose a learning-based correspondence estimation, which focuses on contour points and can be used in combination with the point-to-plane correspondence model-based registration. When combined with classical correspondence estimation in a refinement step, the method highly increases the robustness, leading to a capture range of 36mm and a success rate of 98.5%, compared to 14mm and 71.9% for the purely classical approach, while maintaining a high accuracy of 0.430.08mm of mean re-projection distance.
CRIS-Team Scopus Importer:2020-04-21

Learning-based misalignment detection for 2-D/3-D overlays - Schaffert, Roman; Wang, Jian; Fischer, Peter; Borsdorf, Anja; Maier, Andreas (Informatik aktuell, 2020)
In minimally invasive procedures, a standard routine of observing the operational site is using image guidance. X-ray fluoroscopy using C-arm systems is widely used. In complex cases, overlays of preoperative 3-D images are necessary to show structures that are not visible in the 2-D X-ray images. The alignment quality may degenerate during an intervention, e. g. due to patient motion, and a new registration needs to be performed. However, a decrease in alignment quality is not always obvious, as the clinician often focuses on structures which are not visible in the 2-D image, and only these structures are visualized in the overlay. In this paper, we propose a learning-based method for detecting different degrees of misalignment. The method is based on point-to-plane correspondences and a pre-trained neural network originally used for detecting good correspondences. The network is extended by a classification branch to detect different levels of misalignment. Compared to simply using the normalized gradient correlation similarity measure as a basis for the decision, we show a highly improved performance, e. g. improving the AUC score from 0.918 to 0.993 for detecting misalignment above 5mm of mean re-projection distance.
CRIS-Team Scopus Importer:2020-04-21

Learning-based occupational x-ray scatter estimation - Maul, Noah; Roser, Philipp; Birkhold, Annette; Kowarschik, Markus; Zhong, Xia; Strobel, Norbert; Maier, Andreas (Physics in Medicine and Biology, 2022)
Objective. During x-ray-guided interventional procedures, the medical staff is exposed to scattered ionizing radiation caused by the patient. To increase the staff's awareness of the invisible radiation and monitor dose online, computational scatter estimation methods are convenient. However, such methods are usually based on Monte Carlo (MC) simulations, which are inherently computationally expensive. Yet, in the interventional environment, immediate feedback to the personnel is desirable. Approach. In this work, we propose deep neural networks to mitigate the computational effort of MC simulations. Our learning-based models consider detailed models of the (outer) patient shape and (inner) anatomy, additional objects in the room, and the x-ray tube spectrum to cover imaging settings encountered in real interventional settings. We investigate two cases of scatter prediction. First, we employ network architectures to estimate the full three-dimensional (3D) scatter distribution. Second, we investigate the prediction of two-dimensional (2D) intensity projections that facilitate the intra-procedural visualization. Main results. Depending on the dimensionality of the estimated scatter distribution and the network architecture, the mean relative error of each network is in the range of 12% and 14% compared to MC simulations. However, 3D scatter distributions can be estimated within 60 ms and 2D distributions within 15 ms. Significance. Overall, our method is suitable to support the online assessment of scattered ionizing radiation in the interventional environment and can help to lower the occupational radiation risk.
CRIS-Team WoS Importer:2022-04-01

Learning-based Patch-wise Metal Segmentation with Consistency Check - Gottschalk, Tristan; Maier, Andreas; Kordon, Florian Johannes; Kreher, Björn W. (Bildverarbeitung für die Medizin 2021, 2021)
Metal implants that are inserted into the patient's body during trauma interventions cause heavy artifacts in 3D X-ray acquisitions. Metal Artifact Reduction (MAR) methods, whose first step is always a segmentation of the present metal objects, try to remove these artifacts. Thereby, the segmentation is a crucial task which has strong influence on the MAR's outcome. This study proposes and evaluates a learning-based patch-wise segmentation network and a newly proposed Consistency Check as post-processing step. The combination of the learned segmentation and Consistency Check reaches a high segmentation performance with an average IoU score of 0.924 on the test set. Furthermore, the Consistency Check proves the ability to significantly reduce false positive segmentations whilst simultaneously ensuring consistent segmentation
Keywords: Metal Artifact Reduction; Deep Learning; Consistency Check

Learning-based Trajectory Optimization for a Twin Robotic CT System - Schneider, Linda-Sophie; Thies, Mareike; Schielein, Richard; Syben, Christopher; Unberath, Mathias; Maier, Andreas (NDT.net Issue: 2023-03, 2023)
Non-circular twin robot computed tomography (CT) systems provide a high degree of flexibility in positioning the X-ray source and detector. This makes the design of optimal CT trajectories particularly important, as it can potentially allow for a reduction in the number of projections required for image acquisition. Therefore, the optimization of CT trajectories is a key area of research in this field. This paper presents a method for optimising the trajectory of a non-circular twin robot computed tomography system, using a so-called detectability index to guide the optimization process. The proposed method involves simulating possible X-ray projections that are reachable by the robotic CT system and using a regressive ResNet-34 to predict the detectability index of each projection. An integer optimization problem is then used to generate an optimised CT trajectory based on this prediction, with the haversine distance between projections serving as an additional constraint. The performance of the ResNet-34 is evaluated on unseen projections. It effectively predicted the detectability index for a spherical sampling space, allowing for the optimization of arbitrary CT trajectories. In addition, the method was able to identify valuable projections according to the detectability index, enabling the reconstruction of a test object with only 30 out of 800 of the previously acquired projections without significant loss of information in the reconstruction domain.<br

Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests - Sindel, Aline; Breininger, Katharina; Käßer, Johannes; Heß, Andreas; Maier, Andreas; Köhler, Thomas (25th IEEE International Conference on Image Processing, ICIP 2018, 2018)
Magnetic resonance imaging (MRI) enables 3-D imaging of anatomical structures. However, the acquisition of MR volumes with high spatial resolution leads to long scan times. To this end, we propose volumetric super-resolution forests (VSRF) to enhance MRI resolution retrospectively. Our method learns a locally linear mapping between low-resolution and high-resolution volumetric image patches by employing random forest regression. We customize features suitable for volumetric MRI to train the random forest and propose a median tree ensemble for robust regression. VSRF outperforms state-of-the-art example-based super-resolution in terms of image quality and efficiency for model training and inference on different MRI datasets. It is also superior to unsupervised methods with just a handful or even a single volume to assemble training data.<br
Keywords: MRI; Super-resolution; Random forests

Learning how to Clean Fingerprints – Deep Learning based Separated Artefact Reduction and Regression for MR Fingerprinting - Xu, Yiling; Hoppe, Elisabeth; Speier, Peter; Kluge, Thomas; Nittka, Mathias; Koerzdoerfer, Gregor; Maier, Andreas (International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition, 2020)

Learning how to Clean Fingerprints – Deep Learning based Separated Artefact Reduction and Regression for MR Fingerprinting - Xu, Yiling; Hoppe, Elisabeth; Speier, Peter; Kluge, Thomas; Nittka, Mathias; Koerzdoerfer, Gregor; Maier, Andreas (ISMRM Workshop on Data Sampling & Image Reconstruction, 2020)

Learning the Inverse Weighted Radon Transform - Roser, Philipp; Felsner, Lina; Maier, Andreas; Riess, Christian (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Learning the Update Operator for 2D/3D Image Registration - Jaganathan, Srikrishna; Wang, Jian; Borsdorf, Anja; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories - Zaech, Jan Nico; Gao, Cong; Bier, Bastian; Taylor, Russell; Maier, Andreas; Navab, Nassir; Unberath, Mathias (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction. These artifacts are particularly strong around metal implants, inhibiting widespread adoption of 3D cone-beam CT (CBCT) despite clear opportunity for intra-operative verification of implant positioning, e. g. in spinal fusion surgery. On synthetic and real data, we demonstrate that much of the artifact can be avoided by acquiring better data for reconstruction in a task-aware and patient-specific manner, and describe the first step towards the envisioned task-aware CBCT protocol. The traditional short-scan CBCT trajectory is planar, with little room for scene-specific adjustment. We extend this trajectory by autonomously adjusting out-of-plane angulation. This enables C-arm source trajectories that are scene-specific in that they avoid acquiring “poor images”, characterized by beam hardening, photon starvation, and noise. The recommendation of ideal out-of-plane angulation is performed on-the-fly using a deep convolutional neural network that regresses a detectability-rank derived from imaging physics.
CRIS-Team Scopus Importer:2019-12-10
Keywords: Deep reinforcement learning; Robotic imaging

Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views - Bier, Bastian; Goldmann, Florian; Zaech, Jan Nico; Fotouhi, Javad; Hegeman, Rachel; Grupp, Robert; Armand, Mehran; Osgood, Greg; Navab, Nassir; Maier, Andreas; Unberath, Mathias (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose: Minimally invasive alternatives are now available for many complex surgeries. These approaches are enabled by the increasing availability of intra-operative image guidance. Yet, fluoroscopic X-rays suffer from projective transformation and thus cannot provide direct views onto anatomy. Surgeons could highly benefit from additional information, such as the anatomical landmark locations in the projections, to support intra-operative decision making. However, detecting landmarks is challenging since the viewing direction changes substantially between views leading to varying appearance of the same landmark. Therefore, and to the best of our knowledge, view-independent anatomical landmark detection has not been investigated yet. Methods: In this work, we propose a novel approach to detect multiple anatomical landmarks in X-ray images from arbitrary viewing directions. To this end, a sequential prediction framework based on convolutional neural networks is employed to simultaneously regress all landmark locations. For training, synthetic X-rays are generated with a physically accurate forward model that allows direct application of the trained model to real X-ray images of the pelvis. View invariance is achieved via data augmentation by sampling viewing angles on a spherical segment of 120 ∘ × 90 ∘ . Results: On synthetic data, a mean prediction error of 5.6 ± 4.5 mm is achieved. Further, we demonstrate that the trained model can be directly applied to real X-rays and show that these detections define correspondences to a respective CT volume, which allows for analytic estimation of the 11 degree of freedom projective mapping. Conclusion: We present the first tool to detect anatomical landmarks in X-ray images independent of their viewing direction. Access to this information during surgery may benefit decision making and constitutes a first step toward global initialization of 2D/3D registration without the need of calibration. As such, the proposed concept has a strong prospect to facilitate and enhance applications and methods in the realm of image-guided surgery.
CRIS-Team Scopus Importer:2019-05-02
Keywords: 2D/3D registration; Anatomical landmarks; Convolutional neural networks; Landmark detection

Learning to recognize abnormalities in chest X-rays with location-aware dense networks - Gündel, Sebastian; Grbic, Sasa; Georgescu, Bogdan; Liu, Siqi; Maier, Andreas; Comaniciu, Dorin (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
Chest X-ray is the most common medical imaging exam used to assess multiple pathologies. Automated algorithms and tools have the potential to support the reading workflow, improve efficiency, and reduce reading errors. With the availability of large scale data sets, several methods have been proposed to classify pathologies on chest X-ray images. However, most methods report performance based on random image based splitting, ignoring the high probability of the same patient appearing in both training and test set. In addition, most methods fail to explicitly incorporate the spatial information of abnormalities or utilize the high resolution images. We propose a novel approach based on location aware Dense Networks (DNetLoc), whereby we incorporate both high-resolution image data and spatial information for abnormality classification. We evaluate our method on the largest data set reported in the community, containing a total of 86,876 patients and 297,541 chest X-ray images. We achieve (i) the best average AUC score for published training and test splits on the single benchmarking data set (ChestX-Ray14 [1]), and (ii) improved AUC scores when the pathology location information is explicitly used. To foster future research we demonstrate the limitations of the current benchmarking setup [1] and provide new reference patient-wise splits for the used data sets. This could support consistent and meaningful benchmarking of future methods on the largest publicly available data sets.
CRIS-Team Scopus Importer:2019-08-13

Learning with Known Operators reduces Maximum Training Error Bounds. - Maier, Andreas; Syben-Leisner, Christopher; Stimpel, Bernhard; Würfl, Tobias; Hoffmann, Mathis; Schebesch, Frank; Fu, Weilin; Mill, Leonid; Kling, Lasse; Christiansen, Silke H. (Nature Machine Intelligence, 2019)
We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework. We derive a maximal error bound for deep nets that demonstrates that inclusion of prior knowledge results in its reduction. Furthermore, we also show experimentally that known operators reduce the number of free parameters. We apply this approach to various tasks ranging from CT image reconstruction over vessel segmentation to the derivation of previously unknown imaging algorithms. As such the concept is widely applicable for many researchers in physics, imaging, and signal processing. We assume that our analysis will support further investigation of known operators in other fields of physics, imaging, and signal processing.

Left Ventricle Segmentation in LGE-MRI: Filter Based vs. Learning Based - Kurzendorfer, Tanja; Breininger, Katharina; Steidl, Stefan; Brost, Alexander; Forman, Christoph; Maier, Andreas (2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings, 2018)
Ischaemic heart disease is the number one cause of death world wide, which is in close relation with heart failure. If patients suffer from drug-refractory heart failure with a reduced ejection fraction, cardiac resynchronization therapy is a treatment option. For planning the procedure, precise information about the left ventricle's anatomy and scar distribution is required. The clinical gold standard to visualize scar is late gadolinium enhanced magnetic resonance imaging (LGE-MRI). The challenge arises in the myocardium segmentation of these sequences which is a pre-requisite for an accurate scar quantification. In this work, we compare a filter based approach against a learning based approach for LGE-MRI segmentation. For both approaches the segmentation workflow consists of four major steps. First, the left ventricle is detected. Second, the blood pool is estimated. Third, the endocardium is refined using scar information. Fourth, the epicardium is extracted.The proposed methods were evaluated on 100 clinical LGE-MRI data sets. For the learning based approach a 5-fold nested cross-validation is applied to evaluate the hyper-parameters. The learning based segmentation achieves slightly better results, with a Dice score of 0.82 ± 0.09 for the endocard and 0.81 ± 0.08 for the epicard.
CRIS-Team Scopus Importer:2020-11-20

Left ventricle segmentation in LGE-MRI using multiclass learning - Kurzendorfer, Tanja; Breininger, Katharina; Steidl, Stefan; Maier, Andreas; Fahrig, Rebecca (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019)
Cardiovascular diseases are the major cause of death worldwide. Magnetic resonance imaging (MRI) is often used for the diagnosis of cardiac diseases because of its good soft tissue contrast. Furthermore, the fibrosis characterization of the myocardium can be important for accurate diagnosis and treatment planning. The clinical gold standard to visualize myocardial scarring is late gadolinium enhanced (LGE) MRI. However, the challenge arises in the accurate segmentation of the endocardial and epicardial border because of the smooth transition between the blood pool and scarred myocardium, as contrast agent accumulates in the damaged tissue and leads to hyper-enhancements. An exact segmentation, is essential for the scar tissue quantification. We propose a deep learning-based method to segment the left ventricle's endocardium and epicardium in LGE-MRI. To this end, a multi-scale fully convolutional neural network with skip-connections (U-Net) and residual units is applied to solve the multiclass segmentation problem. As a loss function, weighted cross-entropy is used. The network is trained on 70 clinical LGE MRI sequences, validated with 5, and evaluated with 26 data sets. The approach yields a mean Dice coefficient of 0.90 for the endocard and 0.87 for the epicard. The proposed method segments the endocardium and epicardium of the left ventricle fully automatically with a high accuracy.
CRIS-Team Scopus Importer:2019-07-12
Keywords: Convolutional Neural Network; Late Gadolinium Enhanced; Left Ventricle; Segmentation

Left Ventricular Heart Phantom for Wall Motion Analysis - Müller, Kerstin; Maier, Andreas; Fischer, Peter; Bier, Bastian; Lauritsch, Günter; Schwemmer, Chris; Fahrig, Rebecca; Hornegger, Joachim (2013 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2013)
In interventional cardiology, three-dimensional anatomical and functional information of the cardiac chambers, e.g. the left ventricle, would have an important impact on diagnosis and therapy. With the technology of C-arm CT it is possible to reconstruct intraprocedural 3-D images from angiographic projection data. Due to the long acquisition time of several seconds, motion-related artifacts, like blurring or streaks, occur. Therefore, the heart dynamics need to be taken into account in order to improve the reconstruction results. When it comes to the evaluation of different motion estimation and compensation algorithms and techniques of motion analysis, there is still a lack of comparability of the final reconstructions and motion parameters between the research groups. Since the results are heavily dependent on the applied motion pattern and simulation parameters, the experiments are not reproducible. We try to overcome these problems by providing varying left heart ventricle phantom datasets, consisting of projection images as well as extracted surface meshes. Up to now, there are six different datasets available: one with a normal sinus rhythm, one with a normal sinus rhythm and a catheter, one with a lateral wall defect of the ventricle, two with a lateral contraction phase shift and one without any motion. The existing datasets are based on a phantom similar to the 4D XCAT phantom with a contrasted left ventricle, myocardium, and aorta. The geometry calibration and acquisition protocol from a real clinical C-arm scanner are used. A webpage is provided where the data and the necessary files are publicly available for download at conrad.stanford.edu/data/heart. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.leftve_0

Lesion Ground Truth Estimation for a Physical Breast Phantom - Hanif, Suneeza; Schebesch, Frank; Jerebko, Anna; Ritschl, Ludwig; Mertelmeier, Thomas; Maier, Andreas (Bildverarbeitung für die Medizin 2017 - Algorithmen, Systeme, Anwendungen, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.lesion

Light field processing for media applications - Goldmann, Florian; Keinert, Joachim; Fink, Laura; Gul, Muhammad Shahzeb Khan; Jaschke, Tobias; Ziegler, Matthias; Baetz, Michel; Fößel, Siegfried; Prappacher, Nico (Immersive Video Technologies, 2023)

Lightweight Online Noise Reduction on Embedded Devices using Hierarchical Recurrent Neural Networks - Schröter, Hendrik; Rosenkranz, Tobias; Escalante Banuelos, Alberto; Zobel, Pascal; Maier, Andreas (INTERSPEECH 2020, 2020)
Deep-learning based noise reduction algorithms have proven their success especially for non-stationary noises, which makes it desirable to also use them for embedded devices like hearing aids (HAs). This, however, is currently not possible with state-of-the-art methods. They either require a lot of parameters and computational power and thus are only feasible using modern CPUs. Or they are not suitable for online processing, which requires constraints like low-latency by the filter bank and the algorithm itself. In this work, we propose a mask-based noise reduction approach. Using hierarchical recurrent neural networks, we are able to drastically reduce the number of neurons per layer while including temporal context via hierarchical connections. This allows us to optimize our model towards a minimum number of parameters and floating-point operations (FLOPs), while preserving noise reduction quality compared to previous work. Our smallest network contains only 5k parameters, which makes this algorithm applicable on embedded devices. We evaluate our model on a mixture of EUROM and a real-world noise database and report objective metrics on unseen noise.
Keywords: speech enhancement; noise reduction; recurrent neural networks

Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning - Huang, Yixing; Wang, Shengxiang; Guan, Yong; Maier, Andreas (Journal of Synchrotron Radiation, 2020)
In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision to other system parts or high attenuation at certain tilting angles. Image reconstruction from such limited angle data suffers from artifacts due to missing data. In this work, deep learning is applied to limited angle reconstruction in TXMs for the first time. With the challenge to obtain sufficient real data for training, training a deep neural network from synthetic data is investigated. Particularly, the U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images. The proposed method is evaluated on synthetic data and real scanned chlorella data in 100-degree limited angle tomography. For synthetic test data, the U-Net significantly reduces root-mean-square error (RMSE) from 2.55 X 10^-3/μm in the FBP reconstruction to 1.21 X 10^-3/μm in the U-Net reconstruction, and also improves structural similarity (SSIM) index from 0.625 to 0.920. With penalized weighted least square denoising of measured projections, the RMSE and SSIM are further improved to 1.16 X 10^-3/μm and 0.932, respectively. For real test data, the proposed method remarkably improves the 3-D visualization of the subcellular structures in the chlorella cell, which indicates its important value for nano-scale imaging in biology, nanoscience and materials scienc
Keywords: Transmission X-Ray Microscopy; deep learning; limited angle tomography

Limited parameter denoising for low-dose X-ray computed tomography using deep reinforcement learning - Patwari, Mayank; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (Medical Physics, 2022)
Background The use of deep learning has successfully solved several problems in the field of medical imaging. Deep learning has been applied to the CT denoising problem successfully. However, the use of deep learning requires large amounts of data to train deep convolutional networks (CNNs). Moreover, due to large parameter count, such deep CNNs may cause unexpected results. Purpose In this study, we introduce a novel CT denoising framework, which has interpretable behaviour, and provides useful results with limited data. Methods We employ bilateral filtering in both the projection and volume domains to remove noise. To account for non-stationary noise, we tune the σ parameters of the volume for every projection view, and for every volume pixel. The tuning is carried out by two deep CNNs. Due to impracticality of labelling, the two deep CNNs are trained via a Deep-Q reinforcement learning task. The reward for the task is generated by using a custom reward function represented by a neural network. Our experiments were carried out on abdominal scans for the Mayo Clinic TCIA dataset, and the AAPM Low Dose CT Grand Challenge. Results Our denoising framework has excellent denoising performance increasing the PSNR from 28.53 to 28.93, and increasing the SSIM from 0.8952 to 0.9204. We outperform several state-of-the-art deep CNNs, which have several orders of magnitude higher number of parameters (p-value (PSNR) = 0.000, p-value (SSIM) = 0.000). Our method does not introduce any blurring, which is introduced by MSE loss based methods, or any deep learning artifacts, which are introduced by WGAN based models. Our ablation studies show that parameter tuning and using our reward network results in the best possible results. Conclusions We present a novel CT denoising framework, which focuses on interpretability to deliver good denoising performance, especially with limited data. Our method outperforms state-of-the-art deep neural networks. Future work will be focused on accelerating our method, and generalizing to different geometries and body parts.

Local topology preservation for vascular centerline matching using a hybrid mixture model - Bayer, Siming; Zhai, Zhiwei; Strumia, Maddalena; Tong, Xiaoguang; Gao, Ying; Staring, Marius; Stoel, Berend; Ostermeier, Martin; Fahrig, Rebecca; Arya, Nabavi; Maier, Andreas; Ravikumar, Nishant (Conference Record IEEE 2019 NSSMIC, 2019)
Non-rigid registration is essential for a wide range of clinical applications, such as intraoperative image-guidance and postoperative follow-up assessment, and longitudinal image analysis for disease diagnosis and monitoring. Vascular structures are a rich descriptor of the organ deformation, since it permeates through all organs within body. As vasculature differs in size, shape and topology, following surgical intervention/treatment or due to disease progression, non-rigid vessel matching remains a challenging task. Recently, hybrid mixture models (HdMM) have been applied to tackle this challenge, and demonstrate significant improvements in terms of accuracy and robustness relative to the state-of-the-art. However, the smoothness constraint enforced on the deformation field with this approach only accounts for the global topology of the vasculature, resulting in a reduced capacity to accurately match localized changes to vascular structures, and preserve local topology. In this work, we proposed a modified version of HdMM by formulating an adaptive kernel, to enforce a local smoothness constraint on the deformation field, henceforth referred to as HdMMad. The proposed HdMMad framework is evaluated with cerebral and pulmonary vasculature, acquired retrospectively. The registration results for both data sets demonstrate that the proposed approach outperforms registration algorithms also designed to preserve local topology. Using HdMMad, around 80% of the initial registration error was reduced, for both data sets.<br

Log File Processing - Changes, Challenges, and Chances - Rücker, Nadine (2022)
Log files are plain text files written by any modern computer system. Their content is determined by logging statements during software development. Thus, any kind of event can be collected during runtime. As a result, logs contain a wealth of precious information that is used for various analyses. However, current log processing methods assume static log structures and contents. Due to today’s focus on agile software development paradigms, source code and therein contained logging statements can constantly change. This leads to failing preprocessing and incorrect data points for subsequent analyses. If those failures are not detected, faulty information will be extracted which will result in erroneous insights. This dissertation targets successful log file processing from different angles using artificial intelligence approaches. First, missing data points induced by changing software are addressed. Using Magnetic Resonance Imaging (MRI) examination data as an example, new variables are added to the logging mechanism with growing requirements. As a consequence, logs from systems with earlier software version are missing the new features. Therefore, we propose classification techniques to learn feature correlations from complete data sets. Afterwards, these trained models are applied to impute data points where the respective feature is missing. We prove the effectiveness of a feed forward neural network by successfully determining the desired feature for more than 94% of the MRI exams. Furthermore, missing information can also originate from failing parsers. Despite availability within the raw logs, crucial data points might not be extracted correctly. Since software changes can entail log file structure adaptions, they can pose insurmountable challenges to existing log parsing methods. We investigate Hidden Markov Models and Deep Learning methods in order to process log files automatically and reliably despite those challenges. We study different log file data sets from various systems with manifold log changes and outperform state-of-the-art parsers. Thus, we propose a novel pipeline for flexible parsing. It contains a stateful Long Short-Term Memory (LSTM) network to model adaptability regarding log changes. We call it FlexParser and yield for all studied data sets an F1-Score of 92% and higher. Successfully parsed log files hold manifold chances to find actionable insights. Therefore, we process the parsing results of MRI log files in order to predict hardware failures. High image quality is crucial for medical diagnosis, however, directly depends next to the selected imaging parameters also on flawless hardware. Following, we train Deep Learning models on image features and respective recorded hardware conditions. Since available data points from failing components is limited, we employ different data augmentation techniques. Furthermore, we investigate Ensemble Learning to combine insights from different models and compare results with those achieved by time series methods. Concluding, we propose an Ensemble Learning pipeline which reliably detects hardware failures achieving an F1-Score of 94.14%.<br
Keywords: Log Analytics; Parsing; Predictive Maintenance

Long-term Outcome of Speech Intelligibility in Maxillary Dental Rehabilitation with Full Dentures: A Prospective Study Using Automatic Speech Quantification - Stelzle, Florian; Riemann, Max; Klein, Alfred; Oetter, Nicolai; Rohde, Maximilian; Maier, Andreas; Eitner, Stephan; Neukam, Friedrich Wilhelm; Knipfer, Christian (International Journal of Prosthodontics, 2017)
Complete maxillary edentulism and prosthetic rehabilitation with removable full dentures are known to affect speech intelligibility. The aim of this study was to prospectively investigate the long-term effect of time on speech intelligibility in patients being rehabilitated with newly fabricated full maxillary dentures.Speech was recorded in a group of 14 patients (male = 9, female = 5; mean age ± standard deviation [SD] = 66.14 ± 7.03 years) five times within a mean period of 4 years (mean ± SD: 47.50 ± 18.16 months; minimum/maximum: 24/68 months) and in a control group of 40 persons with healthy dentition (male = 30, female = 10; mean age ± SD = 59 ± 12 years). All 14 participants had their inadequate removable full maxillary dentures replaced with newly fabricated dentures. Speech intelligibility was measured by means of a polyphone-based speech recognition system that automatically computed the percentage of accurately spoken words (word accuracy [WA]) at five different points in time: 1 week prior to prosthetic maxillary rehabilitation (both with and without inadequate dentures in situ) and at 1 week, 6 months, and a mean of 48 months after the insertion of newly fabricated full maxillary dentures.Speech intelligibility of the patients significantly improved after 6 months of adaptation to the new removable full maxillary dentures (WA = 66.93% ± 9.21%) compared to inadequate dentures in situ (WA = 60.12% ± 10.48%). After this period, no further significant change in speech intelligibility was observed. After 1 week of adaptation, speech intelligibility of the rehabilitated patients aligned with that of the control group (WA = 69.79% ± 10.60%) and remained at this level during the examination period of 48 months.The provision of new removable full maxillary dentures can improve speech intelligibility to the level of a healthy control group on a long-term basis.
EVALuna2:24437

Low Dose CT Denoising via Joint Bilateral Filtering and Intelligent Parameter Optimization - Patwari, Mayank; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (Proceeding of the 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020)
Denoising of clinical CT images is an active area for deep learning research. Current clinically approved methods use iterative reconstruction methods to reduce the noise in CT images. Iterative reconstruction techniques require multiple forward and backward projections, which are time-consuming and computationally expensive. Recently, deep learning methods have been successfully used to denoise CT images. However, conventional deep learning methods suffer from the ’black box’ problem. They have low accountability, which is necessary for use in clinical imaging situations. In this paper, we use a Joint Bilateral Filter (JBF) to denoise our CT images. The guidance image of the JBF is estimated using a deep residual convolutional neural network (CNN). The range smoothing and spatial smoothing parameters of the JBF are tuned by a deep reinforcement learning task. Our actor first chooses a parameter, and subsequently chooses an action to tune the value of the parameter. A reward network is designed to direct the reinforcement learning task. Our denoising method demonstrates good denoising performance, while retaining structural information. Our method significantly outperforms state of the art deep neural networks. Moreover, our method has only two parameters, which makes it significantly more interpretable and reduces the ’black box’ problem. We experimentally measure the impact of our intelligent parameter optimization and our reward network. Our studies show that our current setup yields the best results in terms of structural preservation.<br
Keywords: Reinforcement Learning; Precision Learning; Low Dose CT; Denoising; Machine Learning; Deep Learning

Low dose CT denoising with residual convolutional networks and deep learned perceptual loss - Patwari, Mayank; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (European Congress of Radiology, 2020)
In accordance with the ALARA principle, we aim to reduce clinical CT dose to improve patient safety. However, reducing the radiation dose increases quantum noise in the reconstructed images. The use of convolutional neural networks (CNN) for low dose CT denoising has shown promising results in body scanning. Current CNN denoising strategies optimise metrics like the mean squared error, which causes smoothing of edges. We investigate the use of perceptual metrics to train a denoising CNN. Seven CT body scans were reconstructed using weighted filtered backprojection. Each volume was reconstructed with the standard clinical dose and with doses of 50%, 25%, 10% and 5% of the standard clinical dose to simulate reduced dose acquisitions. An eight layer residual CNN was used as our denoising network. The network was trained with mean squared error as well as with the use of a deep learned perceptual quality network. 2D and 3D variants of the denoising networks were trained to compare denoising performance. PSNR and SSIM are used to measure noise and feature preservation respectively. The use of a perceptual loss function improved denoising and feature preservation in a 2D denoising network (SSIM = 0.7020, PSNR = 33.8423) against mean squared error denoising (SSIM = 0.6813, PSNR = 33.7544). Perceptual loss functions improved feature preservation in 3D denoising networks (SSIM = 0.6736, PSNR = 33.9524) against mean squared error denoising (SSIM = 0.6699, PSNR = 33.9605). Using a perceptual loss function improves feature preservation in low dose CT denoising. Our deep learned network captures more information about CT image features and CT image noise compared to the mean squared erro
Keywords: CT denoising; Residual Neural Networks; Perceptual Loss

Low Latency Speech Enhancement for Hearing Aids Using Deep Filtering - Schröter, Hendrik; Rosenkranz, Tobias; Escalante-B, Alberto-N; Maier, Andreas (IEEE/ACM Transactions on Audio, Speech and Language Processing, 2022)
Noise reduction is an important feature supporting hearing aid (HA) users in their daily routines and is thus included in most commercially available devices. Latency requirements of HAs require short processing windows resulting in a poor frequency resolution in the whole processing chain including noise reduction. Previous studies have shown that deep neural network (DNN) based algorithms outperform conventional noise reduction algorithms especially for non-stationary noises. This study explores a DNN based noise reduction method using deep filtering targeted for wideband spectrograms given the employed HA filter bank. That is, we predict complex filter coefficients that are linearly applied to the noisy spectrum. We assess different filter sizes over time and frequency axis, and provide evidence for a superior performance over a complex ratio mask. Furthermore, we introduce a frequency response loss that operates on a per-frequency-band basis to fully utilize the deep filtering concept. We objectively demonstrate on-par performance with related state-of-the-art deep learning methods and show in a subjective user study that our method is perceptually preferred to existing HA noise reduction algorithms.
CRIS-Team WoS Importer:2022-09-02

Low-rank and Sparse Matrix Decomposition for Compressed Sensing reconstruction of Magnetic Resonance 4D Phase Contrast blood flow imaging (LoSDeCoS 4D-PCI) - Hutter, Jana; Schmitt, Peter; Gunhild, Aandal; Greiser, Andreas; Forman, Christoph; Grimm, Robert; Hornegger, Joachim; Maier, Andreas (Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2013)

Machine Learning-based Detection of Spherical Markers in CT Volumes - Rao, Disha; Maass, Nicole; Dennerlein, Frank; Maier, Andreas; Huang, Yixing (Informatik aktuell, 2022)
CRIS-Team Scopus Importer:2022-05-06

Machine learning based estimation of hoarseness severity from sustained vowels - Schraut, Tobias; Schützenberger, Anne; Arias Vergara, Tomás; Kunduk, Melda; Echternach, Matthias; Döllinger, Michael (Journal of the Acoustical Society of America, 2022)
Acoustic assessment of voice impairment is commonly performed by subjective perceptual evaluation of continuous speech based on a grading system such as the roughness, breathiness, hoarseness (RBH) scale. Here, we present an automatic approach to objectively quantify hoarseness severity based on sustained vowels. For this study, a total of 635 recordings of the sustained vowel /a/ were collected. Temporal, spectral and cepstral features were extracted from one second of each recording. All recordings have an assigned RBH value, which was determined subjectively by an expert based on continuous speech of the respective subject. In order to account for the label noise introduced by different valuation bases, subjects were divided into two levels of hoarseness H < 2 and H ≥ 2. Logistic Regression was employed as classification model, using the resulting output probabilities as continuous severity rating. Relevant features were selected using a sequence of filter methods and backward elimination. The original feature set was reduced from 50 to 5 features. A classification accuracy of 86.73% was achieved on the test set. Detailed evaluation of output probabilities shows strong correlation (r = 0.81) with H values, allowing to capture and quantify individual improvement, deterioration or no change of hoarseness at different points in time, e.g. before and after voice therapy. The presented method describes a promising approach for objective evaluation of hoarseness, allowing to quantify treatment progress for patients with voice disorders.<br
Keywords: Speech communication; Acoustics; Vowel systems; Machine learning

Machine learning based estimation of hoarseness severity using sustained vowelsa) - Schraut, Tobias; Schützenberger, Anne; Arias Vergara, Tomás; Kunduk, Melda; Echternach, Matthias; Döllinger, Michael (Journal of the Acoustical Society of America, 2024)
Auditory perceptual evaluation is considered the gold standard for assessing voice quality, but its reliability is limited due to inter-rater variability and coarse rating scales. This study investigates a continuous, objective approach to evaluate hoarseness severity combining machine learning (ML) and sustained phonation. For this purpose, 635 acoustic recordings of the sustained vowel /a/ and subjective ratings based on the roughness, breathiness, and hoarseness scale were collected from 595 subjects. A total of 50 temporal, spectral, and cepstral features were extracted from each recording and used to identify suitable ML algorithms. Using variance and correlation analysis followed by backward elimination, a subset of relevant features was selected. Recordings were classified into two levels of hoarseness, H < 2 and H ≥ 2 , yielding a continuous probability score y ̂ ∈ [ 0 , 1 ] . An accuracy of 0.867 and a correlation of 0.805 between the model's predictions and subjective ratings was obtained using only five acoustic features and logistic regression (LR). Further examination of recordings pre- and post-treatment revealed high qualitative agreement with the change in subjectively determined hoarseness levels. Quantitatively, a moderate correlation of 0.567 was obtained. This quantitative approach to hoarseness severity estimation shows promising results and potential for improving the assessment of voice quality.
CRIS-Team Scopus Importer:2024-02-02

Machine Learning-Based Generalized Model for Finite Element Analysis of Roll Deflection During the Austenitic Stainless Steel 316L Strip Rolling - Lotfinia, Mahshad; Tayebi Arasteh, Soroosh (2021)
During the strip rolling process, a considerable amount of the forces of the material pressure cause elastic deformation on the work-roll, i.e., the deflection process. The uncontrollable amount of the work-roll deflection leads to the high deviations in the permissible thickness of the plate along its width. In the context of the Austenitic Stainless Steels (ASS), due to the instability of the Austenite phase in a cold temperature, cold deformation leads to the production of Strain-Induced Martensite (SIM), which improves the mechanical properties. It leads to the hardening of the ASS 316L during the cold deformation, which causes the Strain-Stress curve of the ASS 316L to behave non-linearly, which distinguishes it from other categories of steels. To account for this phenomenon, we propose to utilize a Machine Learning (ML) method to predict more accurately the flow stress of the ASS 316L during the cold rolling. Furthermore, we conduct various mechanical tensile tests in order to obtain the required dataset, Stress316L, for training the neural network. Moreover, instead of using a constant value of flow stress during the multi-pass rolling process, we use a Finite Difference (FD) formulation of the equilibrium equation in order to account for the dynamic behavior of the flow stress, which leads to the estimation of the mean pressure, which the strip enforces to the rolls during deformation. Finally, using the Finite Element Analysis (FEA), the deflection of the work-roll tools will be calculated. As a result, we end up with a generalized model for the calculation of the roll deflection, specific to the ASS 316L. To the best of our knowledge, this is the first model for ASS 316L which considers dynamic flow stress and SIM of the rolled plate, using FEM and an ML approach, which could contribute to the better design of the tolls. <
Keywords: Roll Deflection; Artificial Neural Networks; Finite Element Methods; Finite Difference Scheme; Austenitic Stainless Steel 316L

Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios - Haas, Oliver; Maier, Andreas; Rothgang, Eva (Frontiers in Reproductive Health, 2021)
HIV/AIDS is an ongoing global pandemic, with an estimated 39 million infected worldwide. Early detection is anticipated to help improve outcomes and prevent further infections. Point-of-care diagnostics make HIV/AIDS diagnoses available both earlier and to a broader population. Wide-spread and automated HIV risk estimation can offer objective guidance. This supports providers in making an informed decision when considering patients with high HIV risk for HIV testing or pre-exposure prophylaxis (PrEP). We propose a novel machine learning method that allows providers to use the data from a patient's previous stays at the clinic to estimate their HIV risk. All features available in the clinical data are considered, making the set of features objective and independent of expert opinions. The proposed method builds on association rules that are derived from the data. The incidence rate ratio (IRR) is determined for each rule. Given a new patient, the mean IRR of all applicable rules is used to estimate their HIV risk. The method was tested and validated on the publicly available clinical database MIMIC-IV, which consists of around 525,000 hospital stays that included a stay at the intensive care unit or emergency department. We evaluated the method using the area under the receiver operating characteristic curve (AUC). The best performance with an AUC of 0.88 was achieved with a model consisting of 53 rules. A threshold value of 0.66 leads to a sensitivity of 98% and a specificity of 53%. The rules were grouped into drug abuse, psychological illnesses (e.g., PTSD), previously known associations (e.g., pulmonary diseases), and new associations (e.g., certain diagnostic procedures). In conclusion, we propose a novel HIV risk estimation method that builds on existing clinical data. It incorporates a wide range of features, leading to a model that is independent of expert opinions. It supports providers in making informed decisions in the point-of-care diagnostics process by estimating a patient's HIV risk.
Keywords: HIV; risk estimation; association rules; bias; clinical data; machine learning; artificial intelligence; incidence rate ratio

Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks - Hoppe, Elisabeth; Thamm, Florian; Körzdörfer, Gregor; Syben-Leisner, Christopher; Schirrmacher, Franziska; Nittka, Mathias; Pfeuffer, Josef; Meyer, Heiko; Maier, Andreas (German Medical Data Sciences: Shaping Change – Creative Solutions for Innovative Medicine, 2019)
Magnetic Resonance Fingerprinting (MRF) is an imaging technique acquiring unique time signals for different tissues. Although the acquisition is highly accelerated, the reconstruction time remains a problem, as the state-of-the-art template matching compares every signal with a set of possible signals. To overcome this limitation, deep learning based approaches, e.g. Convolutional Neural Networks (CNNs) have been proposed. In this work, we investigate the applicability of Recurrent Neural Networks (RNNs) for this reconstruction problem, as the signals are correlated in time. Compared to previous methods based on CNNs, RNN models yield significantly improved results using in-vivo data.
CRIS-Team Scopus Importer:2019-09-24
Keywords: Artificial Neural Networks; Magnetic Resonance Fingerprinting; Magnetic Resonance Fingerprinting Reconstruction; Recurrent Neural Networks

Make the Most of Time: Temporal Extension of the iTV Algorithm for 4D Cardiac C-Arm CT - Haase, Viktor; Taubmann, Oliver; Huang, Yixing; Krings, Gregor; Lauritsch, Günter; Maier, Andreas; Mertins, Alfred (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.maketh

Mammographic breast density classification using a deep neural network: assessment based on inter-observer variability - Kaiser, N.; Fieselmann, A.; Vesal, Sulaiman; Ravikumar, Nishant; Ritschl, L.; Kappler, S.; Maier, Andreas (MEDICAL IMAGING 2019: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT, 2019)
CRIS-Team WoS Importer:2019-09-17

Manifold Learning-based Data Sampling for Model Training - Chen, Shuqing; Dorn, Sabrina; Lell, Michael; Kachelriess, Marc; Maier, Andreas (Informatik-Aktuell, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.manifo_6

Mapping Ensembles of Trees to Sparse, Interpretable Multilayer Perceptron Networks - Rodriguez Salas, Dalia; Mürschberger, Nina; Ravikumar, Nishant; Seuret, Mathias; Maier, Andreas (SN Computer Science, 2020)
Tree-based classifiers provide easy-to-understand outputs. Artificial neural networks (ANN) commonly outperform tree-based classifiers; nevertheless, understanding their outputs requires specialized knowledge in most cases. The highly redundant architecture of ANN is typically designed through an expensive trial-and-error scheme. We aim at (1) investigating whether using ensembles of decision trees to design the architecture of low-redundant, sparse ANN provides better-performing networks, and (2) evaluating whether such trees can be used to provide human-understandable explanations for their outputs. Information about the hierarchy of the features, and how good they are at separating subsets of samples among the classes, is gathered from each branch in an ensemble of trees. This information is used to design the architecture of a sparse multilayer perceptron network. Networks built using our method are called ForestNet. Tree branches corresponding to highly activated neurons are used to provide explanations of the networks’ outputs. ForestNets are able to handle low- and high-dimensional data, as we show on an evaluation using four datasets. Our networks consistently outperformed their respective ensemble of trees and had similar performance to their fully connected counterparts with a significant reduction of connections. Furthermore, our interpretation method seems to provide support for the ForestNet outputs. While ForestNet’s architectures do not allow them yet to capture well the intrinsic variability of visual data, they exhibit very promising results by reducing more than 98% of connections for such visual tasks. Structure similarities between ForestNets and their respective tree ensemble provide means to interpret their outputs.<br
Keywords: Multilayer perceptron; Random forest; Interpretable models; Sparse neural networks; Network architecture; Interpretability

Marker-free motion correction in weight-bearing cone-beam CT of the knee joint - Berger, Martin; Müller, Kerstin; Aichert, André; Unberath, Mathias; Thies, Justus; Choi, Jang-Hwan; Fahrig, Rebecca; Maier, Andreas (Medical Physics, 2016)
Purpose: To allow for a purely image-based motion estimation and compensation in weight-bearing cone-beam computed tomography of the knee joint. Methods: Weight-bearing imaging of the knee joint in a standing position poses additional requirements for the image reconstruction algorithm. In contrast to supine scans, patient motion needs to be estimated and compensated. The authors propose a method that is based on 2D/3D registration of left and right femur and tibia segmented from a prior, motion-free reconstruction acquired in supine position. Each segmented bone is first roughly aligned to the motion-corrupted reconstruction of a scan in standing or squatting position. Subsequently, a rigid 2D/3D registration is performed for each bone to each of K projection images, estimating 6x4xK motion parameters. The motion of individual bones is combined into global motion fields using thin-plate-spline extrapolation. These can be incorporated into a motion-compensated reconstruction in the backprojection step. The authors performed visual and quantitative comparisons between a state-of-the-art marker-based (MB) method and two variants of the proposed method using gradient correlation (GC) and normalized gradient information (NGI) as similarity measure for the 2D/3D registration. Results: The authors evaluated their method on four acquisitions under different squatting positions of the same patient. All methods showed substantial improvement in image quality compared to the uncorrected reconstructions. Compared to NGI and MB, the GC method showed increased streaking artifacts due to misregistrations in lateral projection images. NGI and MB showed comparable image quality at the bone regions. Because the markers are attached to the skin, the MB method performed better at the surface of the legs where the authors observed slight streaking of the NGI and GC methods. For a quantitative evaluation, the authors computed the universal quality index (UQI) for all bone regions with respect to the motion-free reconstruction. The authors quantitative evaluation over regions around the bones yielded a mean UQI of 18.4 for no correction, 53.3 and 56.1 for the proposed method using GC and NGI, respectively, and 53.7 for the MB reference approach. In contrast to the authors registration-based corrections, the MB reference method caused slight nonrigid deformations at bone outlines when compared to a motion-free reference scan. Conclusions: The authors showed that their method based on the NGI similarity measure yields reconstruction quality close to the MB reference method. In contrast to the MB method, the proposed method does not require any preparation prior to the examination which will improve the clinical workflow and patient comfort. Further, the authors found that the MB method causes small, nonrigid deformations at the bone outline which indicates that markers may not accurately reflect the internal motion close to the knee joint. Therefore, the authors believe that the proposed method is a promising alternative to MB motion management.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.marker
Keywords: GRK-1773

Markerless Coil Classification and Localization in a Routine MRI Examination Setting using an RGB-D Camera - Nadar, Gayathri; Zhong, Xia; Maier, Andreas (Bildverarbeitung für die Medizin, 2018)
In a routine MRI scan, a radio-frequency (RF) coil must be selected and placed around the region of interest (ROI). This is a crucial step in the workflow as the accurate coil placement is paramount for obtaining high-quality images. However, in the existing workflow, the position of the coil placement on the patient is estimated empirically by the medical technical assistant (MTA). This routine coil placement process has two shortcomings. On the one hand, the expertise of MTA in coil placement, taking the anatomical difference between patients into account, have a huge impact on the accuracy of the coil placement, and subsequently the image quality. On the other hand, the risk of selecting and placing the incorrect coil should be also be acknowledged. To improve the current workflow and provide feedback ahead of the MRI scans, we use an RGB-D camera to acquire extra information. Using the depth images taken before and after placing the coil, we propose a novel method to classify the coil type and localize the coil position during the coil placement process such that the MTA can place the coil correctly and accurately. We trained and evaluated our method over 100 synthetic data sets. We used two types of coils and placed and deformed them differently according to the anatomical region. The evaluation shows that we can classify the coil type without any error, and localize the coil with a mean translational error of 7.1 cm and mean rotation angle error of 0.025 rad.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.marker

Markov Random Field-based Layer Separation for Simulated X-Ray Image Sequences - Fischer, Peter; Pohl, Thomas; Maier, Andreas; Hornegger, Joachim (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.markov_6

Masking for DDG in SPECT Reduces Influence of Motion Artifacts - Reymann, Maximilian; Vija, A. Hans; Maier, Andreas (IEEE RTSD NSS-MIC 2023, 2023)

Material Decomposition for Energy Resolving Detectors using Weighted Levenberg-Marquardt Optimization - Manhart, Michael; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Proceedings of the 1st Conference on Image Guided Interventions, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.materi_5

Material Decomposition Using Ensemble Learning for Energy-Resolved Computed Tomography - Lu, Yanye; Kowarschik, Markus; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (Proceedings of Fully3D'17, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.materi

Material Decomposition Using Ensemble Learning for Spectral X-ray Imaging - Lu, Yanye; Kowarschik, Markus; Huang, Xiaolin; Chen, Shuqing; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Radiation and Plasma Medical Sciences, 2018)
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.materi

Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising - Husvogt, Lennart; Ploner, Stefan; Chen, Siyu; Stromer, Daniel; Schottenhamml, Julia; Alibhai, A. Yasin; Moult, Eric; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Biomedical Optics Express, 2021)
Optical coherence tomography angiography (OCTA) is a novel and clinically promising imaging modality to image retinal and sub-retinal vasculature. Based on repeated optical coherence tomography (OCT) scans, intensity changes are observed over time and used to compute OCTA image data. OCTA data are prone to noise and artifacts caused by variations in flow speed and patient movement. We propose a novel iterative maximum a posteriori signal recovery algorithm in order to generate OCTA volumes with reduced noise and increased image quality. This algorithm is based on previous work on probabilistic OCTA signal models and maximum likelihood estimates. Reconstruction results using total variation minimization and wavelet shrinkage for regularization were compared against an OCTA ground truth volume, merged from six co-registered single OCTA volumes. The results show a significant improvement in peak signal-to-noise ratio and structural similarity. The presented algorithm brings together OCTA image generation and Bayesian statistics and can be developed into new OCTA image generation and denoising algorithm
Keywords: OCT; OCTA; optical coherence tomography; optical coherence tomography angiography; baysian; reconstruction

Maximum Likelihood Estimation of Head Motion Using Epipolar Consistency - Preuhs, Alexander; Ravikumar, Nishant; Manhart, Michael; Stimpel, Bernhard; Hoppe, Elisabeth; Syben-Leisner, Christopher; Kowarschik, Markus; Maier, Andreas (Informatik aktuell, 2019)
Open gantry C-arm systems that are placed within the interventional room enable 3-D imaging and guidance for stroke therapy without patient transfer. This can profit in drastically reduced time-totherapy, however, due to the interventional setting, the data acquisition is comparatively slow. Thus, involuntary patient motion needs to be estimated and compensated to achieve high image quality. Patient motion results in a misalignment of the geometry and the acquired image data. Consistency measures can be used to restore the correct mapping to compensate the motion. They describe constraints on an idealized imaging process which makes them also sensitive to beam hardening, scatter, truncation or overexposure. We propose a probabilistic approach based on the Student’s t-distribution to model image artifacts that affect the consistency measure without sourcing from motion.
CRIS-Team Scopus Importer:2019-05-14

McLabel: A Local Thresholding Tool for Efficient Semi-automatic Labelling of Cells in Fluorescence Microscopy - Utz, Jonas; Schlereth, Maja; Qiu, Jingna; Thies, Mareike; Wagner, Fabian; Brahim, Oumaima B.; Gu, Mingxuan; Uderhardt, Stefan; Breininger, Katharina (Informatik aktuell, 2023)
In this work, we present a semi-automatic labelling tool for the annotation of complex cellular structures such as macrophages in fluorescence microscopy images. We present McLabel, a napari plugin that allows users to label structures of interest by simply scribbling outlines around the area of interest, using the triangle thresholding method with post-processing to identify the desired structure. Additionally, manual adaption of the threshold allows for quick and fine-grained local correction of the segmentation. The tool is evaluated in a user study with five experts, who annotated images both with and without the tool. The results show that variability in annotations between experts is reduced when the labelling tool is used and annotation time is reduced by a factor of five on average1.
CRIS-Team Scopus Importer:2023-09-29

Measurement of angles in time-of-flight data for the automatic supervision of training exercises - Soutschek, Stefan; Kornhuber, Johannes; Maier, Andreas; Bauer, Sebastian; Kugler, Patrick; Hornegger, Joachim; Bebenek, Michael; Steckmann, Sven; von Stengel, Simon; Kemmler, Wolfgang (2010 4th International Conference on Pervasive Computing Technologies for Healthcare, Pervasive Health 2010, 2010)
In this paper we present a novel automatic system to supervise and support rehabilitation and fitness exercises. Goal of the system is to enable autonomous training of rehabilitation patients to support their recovery from movement restrictions as well as the prevention of age-related diseases such as osteoporosis. The system uses time-of-flight technology in order to measure movement angles in real-time. This novel setup is compared to a standard tracking system using visual markers. The measurements of both systems correlate with r values between 0.99 and 0.97 depending on the task domain. The mean error of the angle measurements is between 13 and 17 degrees.

Measuring CT Reconstruction Quality with Deep Convolutional Networks - Patwari, Mayank; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (Machine Learning for Medical Image Reconstruction, 2019)
With the increasing use of CT in diagnostic imaging, reducing the clinical radiation dose is necessary for ensuring patient safety. Reduced radiation dose results in quantum noise which adversely affects image quality and diagnostic value. Moreover, obtaining high quality images to act as reference images for image quality assessment is difficult. Therefore, automatic no-reference quality assessment of reconstructed images is necessary to preserve diagnostic image quality, while controlling radiation dose. In this work, we investigate the use of a deep convolutional neural network to measure CT image quality. Our developed metric shows concordance with conventional metrics of CT image quality (|r|>" role="presentation">|r|> 0.75, |ρ|>" role="presentation">|ρ|> 0.75). Our metric ranks images in terms of quality highly accurately (τ" role="presentation">τ = 0.98). We measure noise textures and levels not present in our training dataset. Furthermore, the proposed metric shows the improved quality in high dose iteratively reconstructed images, and the reduced quality in low dose images.
Keywords: Quantum noise; Convolutional neural network; Computed tomography; Image quality

Measuring Phonological Precision in Children with Cleft Lip and Palate - Arias Vergara, Tomás; Londoño-Mora, E.; Pérez Toro, Paula Andrea; Schuster, M.; Nöth, Elmar; Orozco Arroyave, Juan Rafael; Maier, Andreas (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2023)
Children with Cleft Lip and Palate (CLP) may experience difficulties in oral communication, leading to other developmental problems such as delayed language acquisition and poor social skills; thus, early treatment is essential for successful speech rehabilitation. In this paper, we propose a methodology for automatically assessing the phonological precision of children with CLP. We propose to use the probabilities obtained from a phonological class recognizer to measure phonological precision during connected speech. Furthermore, we compute the nasal-to-sound ratio to improve the automatic detection of the nasality level. For this, we considered speech recordings of 88 children with CLP, assessed by a clinician according to four nasality levels: normal, mild, moderate, and severe. We obtained an F1-score of up to 0.54 for detecting the nasality level automatically. The results suggest that phonological analysis can be used for individualized speech rehabilitation.
CRIS-Team Scopus Importer:2023-09-29
Keywords: children's speech; cleft lip and palate; deep learning; pathological speech processing; phonological analysis

Measuring surface area of skin lesions with 2D and 3D algorithms - Dastjerdi, Houman Mirzaalian; Toepfer, Dominique; Rupitsch, Stefan; Maier, Andreas (International Journal of Biomedical Imaging, 2019)
Purpose. The treatment of skin lesions of various kinds is a common task in clinical routine. Apart from wound care, the assessment of treatment efficacy plays an important role. In this paper, we present a new approach to measure the skin lesion surface in two and three dimensions. Methods. For the 2D approach, a single photo containing a flexible paper ruler is taken. After semi-automatic segmentation of the lesion, evaluation is based on local scale estimation using the ruler. For the 3D approach, reconstruction is based on Structure from Motion. Roughly outlining the region of interest around the lesion is required for both methods. Results. The measurement evaluation was performed on 117 phantom images and five phantom videos for 2D and 3D approach, respectively. We found an absolute error of 0.99±1.18 cm2 and a relative error 9.89± 9.31% for 2D. These errors are <1 cm2 and <5% for five test phantoms in our 3D case. As expected, the error of 2D surface area measurement increased by approximately 10% for wounds on the bent surface compared to wounds on the flat surface. Using our method, the only user interaction is to roughly outline the region of interest around the lesion. Conclusions. We developed a new wound segmentation and surface area measurement technique for skin lesions even on a bent surface. The 2D technique provides the user with a fast, user-friendly segmentation and measurement tool with reasonable accuracy for home care assessment of treatment. For 3D only preliminary results could be provided. Measurements were only based on phantoms and have to be repeated with real clinical data.
CRIS-Team Scopus Importer:2019-05-14

Medical imaging modalities - Unberath, Mathias; Maier, Andreas (Elsevier, 2023)
This chapter introduces the most common medical imaging modalities, including X-ray imaging, computed tomography, magnetic resonance imaging, functional imaging, ultrasound, and photoacoustic imaging. In addition to describing the energy-tissue interactions that determine image formation, we describe computational methods to describe image quality. Finally, we discuss clinical use cases and explore how different imaging modalities contribute to patient care in both diagnosis and treatment.
CRIS-Team Scopus Importer:2023-11-10
Keywords: CT; Image quality; Imaging; Medical imaging modalities; MRI; US; Vision

Medical Imaging Systems - Maier, Andreas; Steidl, Stefan; Christlein, Vincent; Hornegger, Joachim (Springer, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.introd

Medical Monkeys: A Crowdsourcing Approach to Medical Big Data - Servadei, Lorenzo; Schmidt, Rainer; Eidelloth, Christina; Maier, Andreas (On the Move to Meaningful Internet Systems. OTM 2017 Workshops, 2017)
UnivIS-Import:2019-08-15:Pub.2017.tech.IMMD.IMMD5.medica_7
Keywords: Free innovation; Medical Big Data; Medical innovation; eHealth; Gamification; Open innovation; Medical image segmentation

Melanoma Segmentation and Classification Employing Imaging and Graph Encodings - Rivera Monroy, Luis; Eberhardt, Martin; Ostalecki, Christian; Baur, Andreas; Vera, Julio; Maier, Andreas (Proceedings - 2022 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2022, 2022)
Histopathology imaging is crucial for the diagnosis and the decision on treating associated tissue diseases. For this reason, Computer-assisted approaches have gained popularity and shown promise in tasks such as segmentation and classification. However, collecting a significant amount of data and high-quality annotations remains a challenge. This work describes a pipeline for toponomy profiling on the population of cells that combines melanoma multidimensional fluorescent imaging, a supervised segmentation of the region of interest, and graph unsupervised embedded representation.The Multi-Epitope-Ligand Cartography (MELC) provides a range of data shown to be optimal for solving supervised and unsuper-vised tasks. This imaging technique and the methodology, such as the one presented here, are promising for further improvement in the study of diagnostic techniques and therapies for skin diseases, such as melanoma.
CRIS-Team Scopus Importer:2023-03-07
Keywords: melanoma; microscopy; neural networks; segmentation

Metal-conscious Embedding for CBCT Projection Inpainting - Fan, Fuxin; Wang, Yangkong; Ritschl, Ludwig; Biniazan, Ramyar; Beister, Marcel; Kreher, Björn W.; Huang, Yixing; Kappler, Steffen; Maier, Andreas (2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023)
The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images. In order to reduce metal artifacts, projection in-painting is an essential step in many metal artifact reduction algorithms. In this work, a hybrid network combining the shift window (Swin) vision transformer (ViT) and a convolutional neural network is proposed as a baseline network for the inpainting task. To incorporate metal information for the Swin ViT-based encoder, metal-conscious self-embedding and neighborhood-embedding methods are investigated. Both methods have improved the performance of the baseline network. Furthermore, by choosing appropriate window size, the model with neighborhood-embedding could achieve the lowest mean absolute error of 0.079 in metal regions and the highest peak signal-to-noise ratio of 42.346 in CBCT projections. At the end, the efficiency of metal-conscious embedding on both simulated and real cadaver CBCT data has been demonstrated, where the inpainting capability of the baseline network has been enhanced.<br

Metal Inpainting in CBCT Projections Using Score-based Generative Model - Mei, Siyuan; Fan, Fuxin; Maier, Andreas (2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023)
During the orthopaedic surgery, the insertion of metallic implants or screws is often performed under mobile C-arm systems. However, due to the high attenuation of metals, severe metal artifacts occur in 3D reconstructions, which degrade the image quality significantly. Therefore, many metal artifact reduction algorithms have been developed to reduce the artifacts, and metal inpainting in the projection domain is an essential step. In this work, a score-based generative model is trained on simulated knee projections, and the in-painted images are obtained by removing the noise in the conditional resampling process. The result implies that the inpainted images by the score-based generative model have more detailed information and achieve the lowest mean absolute error of 0.069 and the highest peak-signal-to-noise ratio of 43.07 compared with the inverse distance weighting interpolation method and the mask pyramid network. Besides, the score-based model can also recover projections with large circular and rectangular masks, showing its generalization in in-painting tasks.<br

Method for comparison of data driven gating algorithms in emission tomography - Reymann, Maximilian; Vija, A. Hans; Maier, Andreas (Physics in Medicine and Biology, 2023)
Objective: Multiple algorithms have been proposed for Data Driven Gating (DDG) in Single Photon Emission Computed Tomography (SPECT) and have successfully been applied to Myocardial Perfusion Imaging (MPI). Application of DDG to acquisition types other than SPECT MPI has not been demonstrated so far, as limitations and pitfalls of current methods are unknown. Approach: We create a comprehensive set of phantoms simulating the influence of different motion artifacts, view angles, moving objects, contrast, and count levels in SPECT. We perform Monte Carlo Simulation (MCS) of the phantoms, allowing the characterization of DDG algorithms using quantitative metrics derived from the data and evaluate the Center of Light (COL) and Laplacian Eigenmaps (LE) methods as sample DDG algorithms. Main results: View angle, object size, count rate density, and contrast influence the accuracy of both DDG methods. Moreover, the ability to extract the respiratory motion in the phantom was shown to correlate with the contrast of the moving feature to the background, the Signal to Noise ratio, and the noise in the data. Significance: We showed that reporting the average correlation to an external physical reference signal per acquisition is not sufficient to characterize DDG methods. Assessing DDG methods on a view-by-view basis using the simulations and metrics from this work could enable the identification of pitfalls of current methods, and extend their application to acquisitions beyond SPECT MPI.

Methods for Weight-bearing C-arm Cone-Beam Computed Tomography of the Knees - Maier, Jennifer (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2021)
Weight-bearing C-arm cone-beam computed tomography (CBCT) of the knees is an imaging technique that can be applied to acquire information about the structures of the knee joint under natural loading conditions. An analysis of the knee joints under load can provide valuable information about the disease progression of patients suffering from osteoarthritis (OA). OA is a degenerative disease that causes breakdown of soft tissues like articular cartilage leading to severe pain especially during movement. Weight-bearing CBCT is beneficial compared with standing 2-D radiographs traditionally applied for OA diagnosis, as it provides 3-D information about the structures of the knees. However, this acquisition technique also poses some challenges which are addressed in this thesis, including detector saturation, motion artifacts owing to postural sway during the scan, and the analysis of the resulting reconstructions. During weight-bearing CBCT acquisition, the C-arm rotates on a horizontal trajectory around the knees. The high X-ray dose required in lateral views to penetrate through both femurs leads to detector saturation in less dense tissue regions. To address this issue, an approach for preventing detector saturation in the analog domain is presented. It non-linearly transforms the intensities using an analog tone mapping operator (TMO) before digitization in order to increase the dynamic range of the detector. Furthermore, a second approach is described that replaces saturated regions in the projection images with information obtained from an additional lowdose scan. A marker-based 3-D non-rigid alignment step makes the approach robust to subject motion between scans. Both approaches lead to improved image quality in simulations, and a clinical evaluation confirms the feasibility of the second approach. The swaying motion of naturally standing subjects during weight-bearing CBCT acquisition leads to blurring and streaking artifacts in the reconstructions. To correct these artifacts, an inertial measurement unit (IMU)-based rigid and non-rigid motion compensation is developed and evaluated in a simulation study using the recorded motion of real standing subjects. The approaches lead to improved image quality on optimal simulations. A noisy signal simulation reveals the limitations of the approach towards an application in real acquisitions. A subsequent phantom study shows that additional motion is induced by the vibration of the C-arm during the scan, which can not be measured by the IMU attached to the legs of the scanned subject. Afterwards, the thickness analysis of tibial cartilage is addressed. First, an analysis of manual segmentations of the tibial cartilage surface of multiple raters is performed showing that low-pass filtered single-rater segmentations are more similar to the consensus of multiple raters than the original segmentations. Furthermore, as a fast and repeatable alternative to manual segmentations, an automatic convolutional neural network (CNN)-based approach for cartilage surface segmentation is developed. As there is no standard measure for cartilage thickness in literature, the results of four cartilage thickness measures are compared revealing their similarities and differences. A subsequent evaluation of the change in cartilage thickness over time supports the expectation that lateral cartilage thickness decreases under load. This thesis provides valuable tools for the pipeline aimed at analyzing cartilage in OA. The presented methods contribute to the improvement of data acquisition and processing in weight-bearing CBCT and pave the road for the evaluation of clinical data through a detailed and thorough analysis of all described processing steps.

Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration - Schaffert, Roman; Wang, Jian; Fischer, Peter; Borsdorf, Anja; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
Registration of pre-operative 3-D volumes to intra-operative 2-D X-ray images is important in minimally invasive medical procedures. Rigid registration can be performed by estimating a global rigid motion that optimizes the alignment of local correspondences. However, inaccurate correspondences challenge the registration performance. To minimize their influence, we estimate optimal weights for correspondences using PointNet. We train the network directly with the criterion to minimize the registration error. We propose an objective function which includes point-to-plane correspondence-based motion estimation and projection error computation, thereby enabling the learning of a weighting strategy that optimally fits the underlying formulation of the registration task in an end-to-end fashion. For single-vertebra registration, we achieve an accuracy of ± mm and highly improved robustness. The success rate is increased from 79.3% to 94.3% and the capture range from 3 mm to 13 mm.
CRIS-Team Scopus Importer:2019-06-21
Keywords: 2-D/3-D registration; Deep learning; Medical image registration; Point-to-plane correspondence model

Microscopy - Mualla, Firas; Aubreville, Marc; Maier, Andreas (Medical Imaging Systems: An Introductory Guide, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.micros

"MigraineMonitor" – Towards a System for the Prediction of Migraine Attacks using Electrostimulation - Stefke, Andrea; Wilm, Frauke; Richer, Robert; Gradl, Stefan; Eskofier, Björn; Forster, Clemens; Namer, Barbara (Current Directions in Biomedical Engineering, 2018)
Migraine attacks can be accompanied by many different symptoms, some of them appearing within 24 hours before the onset of the headache. In previous work, reduced habituation to an electrical pain stimulus at the head was observed in the pre-ictal phase within 24 hours before the headache attack. Based on these results, this work presents an application to track influence factors on migraine attacks and an Arduino-based control unit which replaces the traditional approach of manual electrical stimulation. The usability of both components of the project was evaluated in separate user studies. Results of the usability study show a good acceptance of the systems with a mean SUS score of 92.4. Additionally, they indicate that the developed control unit may substitute the current manual electrical stimulation. Overall, the designed system allows standardized repeatable measurements and is a first step towards the home-use of a device for establishing a new method for migraine prediction.
Keywords: migraine prediction; migraine tracking; premonitory symptoms; electrostimulation; smartphone application

Mind the Gap: Scanner-Induced Domain Shifts Pose Challenges for Representation Learning in Histopathology - Wilm, Frauke; Fragoso, Marco; Bertram, Christof A.; Stathonikos, Nikolas; Ottl, Mathias; Qiu, Jingna; Klopfleisch, Robert; Maier, Andreas; Aubreville, Marc; Breininger, Katharina (Proceedings - International Symposium on Biomedical Imaging, 2023)
Computer-aided systems in histopathology are often challenged by various sources of domain shift that impact the performance of these algorithms considerably. We investigated the potential of using self-supervised pre-training to overcome scanner-induced domain shifts for the downstream task of tumor segmentation. For this, we present the Barlow Triplets to learn scanner-invariant representations from a multi-scanner dataset with local image correspondences. We show that self-supervised pre-training successfully aligned different scanner representations, which, interestingly only results in a limited benefit for our downstream task. We thereby provide insights into the influence of scanner characteristics for downstream applications and contribute to a better understanding of why established self-supervised methods have not yet shown the same success on histopathology data as they have for natural images.
Created from Fastlane, Scopus look-up
Keywords: Barlow Twins; Domain Shift; Histopathology; Representation Learning

Mitigating Medialness Responses from Non-tubular Structures Using Entropy - Unberath, Mathias; Goppert, Eric; Taubmann, Oliver; Maier, Andreas (Computer Assisted Radiology and Surgery (CARS) 2016: Proceedings of the 30th Congress and Exhibition, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.mitiga

Mitosis domain generalization in histopathology images — The MIDOG challenge - Aubreville, Marc; Stathonikos, Nikolas; Bertram, Christof A.; Klopfleisch, Robert; ter Hoeve, Natalie; Ciompi, Francesco; Wilm, Frauke; Marzahl, Christian; Donovan, Taryn A.; Maier, Andreas; Breen, Jack; Ravikumar, Nishant; et al. (Medical Image Analysis, 2023)
The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by pathologists is subject to a strong inter-rater bias, limiting its prognostic value. State-of-the-art deep learning methods can support experts but have been observed to strongly deteriorate when applied in a different clinical environment. The variability caused by using different whole slide scanners has been identified as one decisive component in the underlying domain shift. The goal of the MICCAI MIDOG 2021 challenge was the creation of scanner-agnostic MF detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were provided. In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. The winning algorithm yielded an F1 score of 0.748 (CI95: 0.704-0.781), exceeding the performance of six experts on the same task.
CRIS-Team Scopus Importer:2022-12-16
Keywords: Challenge; Deep Learning; Domain generalization; Histopathology; Mitosis

Model-Based and Data-Driven Geometry Alignment for Angiography Systems - Preuhs, Alexander (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2020)
omography is the reconstruction of internal structures based on a sensory signal. X-ray cone-beam computed tomography (CBCT) measures the X-ray absorption with a source-detector gantry mounted on a C-shaped arm that rotates around the object. During the rotation, projection images are acquired. To obtain a tomographic reconstruction, the projection data is filtered and back-projected using the geometry information from the scan trajectory. This technique is of high relevance for stroke applications, where a hemorrhagic stroke is to be distinguished from an ischemic stroke. By performing the stroke diagnostic on the interventional CBCT system, no patient transfer is required which minimizes the door-to-groin time. However, due to the prolonged acquisition time of CBCT compared to diagnostic computed tomography (CT), the likelihood of patient motion increases. This provides the demand for rigid motion compensation and geometry alignment algorithms. In this thesis, we investigate model-based and data-driven approaches for the compensation of geometry misalignment. Grangeat’s theorem provides a measure to compare the geometric consistency of two projections. In an ideal scenario, the derivative of corresponding epipolar line integrals is equal. Geometric inaccuracies and motion lead to unmatched epipolar line pairs and impair a corresponding consistency measure. We present algorithmic extensions for the computation of Grangeat’s consistency based on a pair of projection images. Using these extensions, we derive analytic motion gradients describing the change of consistency with respect to a rigid motion. Grangeat’s theorem is known to be eÿcient for out-plane motion compensation within circular trajectories, however, in-plane motion is barely detectable. We devise the X-trajectory which is sensitive to both, in-plane and out-plane motion. The X-trajectory can be computed from a regular short-scan, if the object is symmetric, by mirroring the trajectory at the symmetry plane. This requires the estimation of the object’s symmetry plane. We show that Grangeat’s theorem is suitable for the estimation of symmetry planes using an anthropomorphic head phantom. With experiments using digital phantoms and real acquisitions, we show that the X-trajectory improves in-plane motion com-pensation. Hence, symmetry is a powerful concept with encouraging initial results when applied to head imaging, which is worth further investigations to validate its clinical applicability. We also investigate data-driven approaches, where we learn a cost function di-rectly from the clinical data. In this context, we use an appearance learning ap-proach, where a network is applied to regress the reprojection error (RPE) from the reconstructed slice images. The RPE is available during training of the network, be-cause the ground-truth geometry as well as the motion-affected geometry are known. The RPE measures the reconstruction-relevant deviations and is convex. We devise a siamese triplet network to regress the mean reprojection error (mRPE) as well as a projectionwise RPE. The latter provides a rough estimate which parts of the trajec-tory are affected by motion, whereas the mRPE is used as optimization target. The motion compensated geometry is found by a constraint optimization of the mRPE, where only projections are optimized that are classified by the network as motion-corrupted. Using a motion compensation benchmark we show that our approach is superior compared to state-of-the-art image quality metrics (IQMs)<br

Modularization of deep networks allows cross-modality reuse: lesson learnt - Fu, Weilin; Husvogt, Lennart; Ploner, Stefan; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2020)
Fundus photography and Optical Coherence Tomography Angiography (OCT-A) are two commonly used modalities in ophthalmic imaging. With the development of deep learning algorithms, fundus image processing, especially retinal vessel segmentation, has been extensively studied. Built upon the known operator theory, interpretable deep network pipelines with well-defined modules have been constructed on fundus images. In this work, we firstly train a modularized network pipeline for the task of retinal vessel segmentation on the fundus database DRIVE. The pretrained preprocessing module from the pipeline is then directly transferred onto OCT-A data for image quality enhancement without further fine-tuning. Output images show that the preprocessing net can balance the contrast, suppress noise and thereby produce vessel trees with improved connectivity in both image modalities. The visual impression is confirmed by an observer study with five OCT-A experts. Statistics of the grades by the experts indicate that the transferred module improves both the image quality and the diagnostic quality. Our work provides an example that modules within network pipelines that are built upon the known operator theory facilitate cross-modality reuse without additional training or transfer learning.
CRIS-Team Scopus Importer:2020-04-21

Module-Power Prediction from PL Measurements using Deep Learning - Hoffmann, Mathis; Hepp, Johannes; Doll, Bernd; Buerhop-Lutz, Claudia; Peters, Ian Marius; Brabec, Christoph; Maier, Andreas; Christlein, Vincent (IEEE Photovoltaic Specialists Conference, 2021)
The individual causes for power loss of photovoltaic modules are investigated for quite some time. Recently, it has been shown that the power loss of a module is, for example, related to the fraction of inactive areas. While these areas can be easily identified from electroluminescense (EL) images, this is much harder for photoluminescence (PL) images. With this work, we close the gap between power regression from EL and PL images. We apply a deep convolutional neural network to predict the module power from PL images with a mean absolute error (MAE) of 4.4% or 11.7WP. Furthermore, we depict that regression maps computed from the embeddings of the trained network can be used to compute the localized power loss. Finally, we show that these regression maps can be used to identify inactive regions in PL images as wel
Keywords: photoluminescence; power; regression; deep learning; weakly supervised

Monocular multi-person pose estimation: A survey - Dos Reis, Eduardo Souza; Seewald, Lucas Adams; Antunes, Rodolfo Stoffel; Rodrigues, Vinicius Facco; Righi, Rodrigo Da Rosa; Da Costa, Cristiano Andre; Da Silveira Jr, Luiz Gonzaga; Eskofier, Björn; Maier, Andreas; Horz, Tim; Fahrig, Rebecca (Pattern Recognition, 2021)
Multi-person pose estimation in unconstrained scenarios, with an unknown number of individuals, is a main step towards scene understanding and action recognition. Due to the recent advancements on the architecture of convolutional networks, body part detectors are now accurate and estimate poses in real-time for both single-and multi-person scenes. In contrast, assigning detected body parts to coherent human poses when there are multiple persons interacting is an arduous task. To name a few of the challenges that arise in such scenes: person-to-person occlusion, truncated body parts, and more sources for double counting. Recently, the community contributed towards solving most of them. Hence, it would be interesting to analyze and compile successful approaches from current literature into research trends, and identify possible gaps for future works. To the best of our knowledge, there is no up-to-date review on the main advancements in the field that target this particular set of challenges. This survey fills this gap by reviewing the main breakthroughs on multi-person pose estimation over the last decade and summarizing their impact on the state-of-the-art. Regarding scientific contributions, we propose a novel taxonomy that categorizes the reviewed methods according to their main contributions to the pose estimation pipeline, lists the main datasets and evaluation metrics to train new models, and provides insights on the best entries of publicly available benchmarks. (c) 2021 Elsevier Ltd. All rights reserved.
CRIS-Team WoS Importer:2021-06-25
Keywords: Human pose estimation; Multi-person pose estimation; Markerless body part detectors; Human pose tracking

Monte Carlo Dose Simulation for In-Vivo X-Ray Nanoscopy - Wagner, Fabian; Thies, Mareike; Karolczak, Marek; Pechmann, Sabrina; Huang, Yixing; Gu, Mingxuan; Kling, Lasse; Weidner, Daniela; Aust, Oliver; Schett, Georg; Christiansen, Silke; Maier, Andreas (Informatik aktuell, 2022)
CRIS-Team Scopus Importer:2022-05-06

MoPED: Motion Parameter Estimation DenseNet for accelerating retrospective motion correction - Hoßbach, Julian; Splitthoff, Daniel; Cauley, Stephen; Maier, Andreas (ISMRM & SMRT Virtual Conference & Exhibition, 2020)

More than the Name of the Rose - van Erp, Marieke; Tullett, William; Christlein, Vincent; Ehrhart, Thibault; Hürriyetoğlu, Ali; Leemans, Inger; Lisena, Pasquale; Menini, Stefano; Schwabe, Daniel; Tonelli, Sara; Troncy, Raphaël; Zinnen, Mathias (American Historical Review, 2023)

Motion Artifact Detection in Confocal Laser Endomicroscopy Images - Stöve, Maike; Aubreville, Marc; Oetter, Nicolai; Knipfer, Christian; Neumann, Helmut; Stelzle, Florian; Maier, Andreas (Bildverarbeitung für die Medizin 2018. Informatik aktuell., 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.motion

Motion Compensated Fan-Beam CT by Enforcing Fourier Properties of the Sinogram - Berger, Martin; Maier, Andreas; Xia, Yan; Hornegger, Joachim; Fahrig, Rebecca (Proceedings of the third international conference on image formation in x-ray computed tomography, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.motion_3
Keywords: GRK-1773

Motion Compensation for Cone-Beam CT using Fourier Consistency Conditions - Berger, Martin; Xia, Yan; Aichinger, Wolfgang; Mentl, Katrin; Unberath, Mathias; Aichert, André; Riess, Christian; Hornegger, Joachim; Fahrig, Rebecca; Maier, Andreas (Physics in Medicine and Biology, 2017)
Keywords: cone-beam CT; Fourier consistency conditions; motion correction; motion estimation

Motion Compensation using Range Imaging in C-arm Cone-Beam CT - Bier, Bastian; Unberath, Mathias; Geimer, Tobias; Maier, Jennifer; Gold, Garry; Levenston, Marc; Fahrig, Rebecca; Maier, Andreas (Medical Image Understanding and Analysis, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.motion_1

Motion correction and volume merging of ultrahigh resolution OCT enable 3D visualization and longitudinal tracking of hyperreflective foci - Won, Jungeun; Yaghy, Antonio; Ploner, Stefan; Takahashi, Hiroyuki; Reimann, Marcel Kim; Girgis, Jessica M.; Lam, Kenneth; Hwang, Yunchan; Chen, Siyu; Maier, Andreas; Waheed, Nadia K.; Fujimoto, James G. (Investigative Ophthalmology & Visual Science, 2023)

Motion Estimation in Rotational Angiography with alpha–Expansion Moves - Felsner, Lina; Aichert, André; Unberath, Mathias; Maier, Andreas (IEEE Nuclear Science Symposium and Medical Imaging Conference, 2017)

Motion Gradients for Epipolar Consistency - Preuhs, Alexander; Manhart, Michael; Hoppe, Elisabeth; Kowarschik, Markus; Maier, Andreas (15th International Meeting on Fully Three-Dimensional Image Reconstruction, 2019)

Move Over There: One-Click Deformation Correction for Image Fusion During Endovascular Aortic Repair - Breininger, Katharina; Pfister, Marcus; Kowarschik, Markus; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020)
Fusing intraoperative X-ray with information from preoperative computed tomography for endovascular aortic repair has been shown to reduce radiation exposure, need for contrast agent, and procedure time. However, due to the instruments inserted during the intervention, the vasculature deforms and the fusion loses accuracy. In this paper, we propose an approach to use minimal user input obtained from a single 2D image for deformation correction in 3D. We integrate the 2D positional information as a projective constraint in an as-rigid-as-possible deformation model that allows for intraoperative correction of the deformation. Our method achieves clinically relevant accuracies while keeping user inputs to a minimum. We are able to recover the deformation at the right and left internal iliac bifurcation up to a 3D error of 1.9mm, with an error of 0.5mm orthogonal to the viewing direction, and an error of 1.7mm in depth, while keeping the mean computation time below 6s.
CRIS-Team Scopus Importer:2020-11-20
Keywords: Deformation modeling; Endovascular aortic repair; EVAR; Fluoroscopy; Image fusion

Move Over There One-click Deformation Correction for Image Fusion during Endovascular Aortic Repair - Breininger, Katharina; Pfister, Marcus; Kowarschik, Markus; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

MR-contrast-aware image-to-image translations with generative adversarial networks - Denck, Jonas; Guehring, Jens; Maier, Andreas; Rothgang, Eva (International Journal of Computer Assisted Radiology and Surgery, 2021)
Purpose: A magnetic resonance imaging (MRI) exam typically consists of several sequences that yield different image contrasts. Each sequence is parameterized through multiple acquisition parameters that influence image contrast, signal-to-noise ratio, acquisition time, and/or resolution. Depending on the clinical indication, different contrasts are required by the radiologist to make a diagnosis. As MR sequence acquisition is time consuming and acquired images may be corrupted due to motion, a method to synthesize MR images with adjustable contrast properties is required. Methods: Therefore, we trained an image-to-image generative adversarial network conditioned on the MR acquisition parameters repetition time and echo time. Our approach is motivated by style transfer networks, whereas the “style” for an image is explicitly given in our case, as it is determined by the MR acquisition parameters our network is conditioned on. Results: This enables us to synthesize MR images with adjustable image contrast. We evaluated our approach on the fastMRI dataset, a large set of publicly available MR knee images, and show that our method outperforms a benchmark pix2pix approach in the translation of non-fat-saturated MR images to fat-saturated images. Our approach yields a peak signal-to-noise ratio and structural similarity of 24.48 and 0.66, surpassing the pix2pix benchmark model significantly. Conclusion: Our model is the first that enables fine-tuned contrast synthesis, which can be used to synthesize missing MR-contrasts or as a data augmentation technique for AI training in MRI. It can also be used as basis for other image-to-image translation tasks within medical imaging, e.g., to enhance intermodality translation (MRI → CT) or 7 T image synthesis from 3 T MR images.
CRIS-Team Scopus Importer:2021-07-02
Keywords: Deep learning; Generative adversarial networks; Image synthesis; Magnetic resonance imaging

MR-projection imaging with perspective distortion as in X-ray fluoroscopy for interventional X/MR-hybrid applications - Lommen, Jonathan; Syben-Leisner, Christopher; Stimpel, Bernhard; Bayer, Siming; Nagel, Armin Michael; Fahrig, Rebecca; Dörfler, Arnd; Maier, Andreas (MR-projection imaging with perspective distortion as in X-ray fluoroscopy for interventional X/MR-hybrid applications, 2018)

MR to X-ray Projection Image Synthesis - Stimpel, Bernhard; Syben-Leisner, Christopher; Würfl, Tobias; Mentl, Katrin; Dörfler, Arnd; Maier, Andreas (Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography (CT-Meeting), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.mrtoxr_0

MR-zero meets RARE MRI: Joint optimization of refocusing flip angles and neural networks to minimize T2-induced blurring in spin echo sequences - Dang, Hoai Nam; Endres, Jonathan; Weinmüller, Simon; Glang, Felix; Loktyushin, Alexander; Scheffler, Klaus; Dörfler, Arnd; Schmidt, Manuel; Maier, Andreas; Zaiß, Moritz (Magnetic Resonance in Medicine, 2023)
Purpose: An end-to-end differentiable 2D Bloch simulation is used to reduce T2 induced blurring in single-shot turbo spin echo sequences, also called rapid imaging with refocused echoes (RARE) sequences, by using a joint optimization of refocusing flip angles and a convolutional neural network. Methods: Simulation and optimization were performed in the MR-zero framework. Variable flip angle train and DenseNet parameters were optimized jointly using the instantaneous transverse magnetization, available in our simulation, at a certain echo time, which serves as ideal blurring-free target. Final optimized sequences were exported for in vivo measurements at a real system (3 T Siemens, PRISMA) using the Pulseq standard. Results: The optimized RARE was able to successfully lower T2-induced blurring for single-shot RARE sequences in proton density-weighted and T2-weighted images. In addition to an increased sharpness, the neural network allowed correction of the contrast changes to match the theoretical transversal magnetization. The optimization found flip angle design strategies similar to existing literature, however, visual inspection of the images and evaluation of the respective point spread function demonstrated an improved performance. Conclusions: This work demonstrates that when variable flip angles and a convolutional neural network are optimized jointly in an end-to-end approach, sequences with more efficient minimization of T2-induced blurring can be found. This allows faster single- or multi-shot RARE MRI with longer echo trains.
CRIS-Team Scopus Importer:2023-07-07
Keywords: DenseNet; joint-optimization; MR-zero; RARE; RF-pulse design; T2-blurring; TSE

Multi-channel spectrograms for speech processing applications using deep learning methods - Arias-Vergara, T.; Klumpp, Philipp; Vasquez-Correa, J. C.; Nöth, Elmar; Orozco-Arroyave, J. R.; Schuster, M. (Pattern Analysis and Applications, 2020)
Time–frequency representations of the speech signals provide dynamic information about how the frequency component changes with time. In order to process this information, deep learning models with convolution layers can be used to obtain feature maps. In many speech processing applications, the time–frequency representations are obtained by applying the short-time Fourier transform and using single-channel input tensors to feed the models. However, this may limit the potential of convolutional networks to learn different representations of the audio signal. In this paper, we propose a methodology to combine three different time–frequency representations of the signals by computing continuous wavelet transform, Mel-spectrograms, and Gammatone spectrograms and combining then into 3D-channel spectrograms to analyze speech in two different applications: (1) automatic detection of speech deficits in cochlear implant users and (2) phoneme class recognition to extract phone-attribute features. For this, two different deep learning-based models are considered: convolutional neural networks and recurrent neural networks with convolution layers.
CRIS-Team Scopus Importer:2020-10-02
Keywords: Cochlear implants; Multi-channel spectrograms; Phoneme recognition; Speech processing

Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-Beam CT - Maier, Jennifer; Rivera Monroy, Luis; Syben-Leisner, Christopher; Jeon, Yejin; Choi, Jang-Hwan; Hall, Mary; Levenston, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (Bildverarbeitung für die Medizin 2020, 2020)
Analyzing knee cartilage thickness and strain under load can help to further the understanding of the effects of diseases like Osteoarthritis. A precise segmentation of the cartilage is a necessary prerequisite for this analysis. This segmentation task has mainly been addressed in Magnetic Resonance Imaging, and was rarely investigated on contrast-enhanced Computed Tomography, where contrast agent visualizes the border between femoral and tibial cartilage. To overcome the main drawback of manual segmentation, namely its high time investment, we propose to use a 3D Convolutional Neural Network for this task. The presented architecture consists of a V-Net with SeLu activation, and a Tversky loss function. Due to the high imbalance between very few cartilage pixels and many background pixels, a high false positive rate is to be expected. To reduce this rate, the two largest segmented point clouds are extracted using a connected component analysis, since they most likely represent the medial and lateral tibial cartilage surfaces. The resulting segmentations are compared to manual segmentations, and achieve on average a recall of 0.69, which confirms the feasibility of this approach.<br

Multi-Dimensional Flow-Preserving Compressed Sensing (MuFloCoS) for Time-Resolved Velocity-Encoded Phase Contrast MRI - Hutter, Jana; Schmitt, Peter; Saake, Marc; Stübinger, Axel; Grimm, Robert; Forman, Christoph; Greiser, Andreas; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Medical Imaging, 2015)
4-D time-resolved velocity-encoded phase-contrast MRI (4-D PCI) is a fully non-invasive technique to assess hemodynamics in vivo with a broad range of potential applications in multiple cardiovascular diseases. It is capable of providing quantitative flow values and anatomical information simultaneously. The long acquisition time, however, still inhibits its wider clinical use. Acceleration is achieved at present using parallel MRI (pMRI) techniques which can lead to substantial loss of image quality for higher acceleration factors. Both the high-dimensionality and the significant degree of spatio-temporal correlation in 4-D PCI render it ideally suited for recently proposed compressed sensing (CS) techniques. We propose the Multi-Dimensional Flow-preserving Compressed Sensing (MuFloCoS) method to exploit these properties. A multi-dimensional iterative reconstruction is combined with an interleaved sampling pattern (I-VT), an adaptive masked and weighted temporal regularization (TMW) and fully automatically obtained vessel-masks. The performance of the novel method was analyzed concerning image quality, feasibility of acceleration factors up to 15, quantitative flow values and diagnostic accuracy in phantom experiments and an in vivo carotid study with 18 volunteers. Comparison with iterative state-of-the-art methods revealed significant improvements using the new method, the temporal normalized root mean square error of the peak velocity was reduced by 45.32% for the novel MuFloCoS method with acceleration factor 9. The method was furthermore applied to two patient cases with diagnosed high-grade stenosis of the ICA, which confirmed the performance of MuFloCoS to produce valuable results in the presence of pathological findings in 56 s instead of over 8 min (full sampling).
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.multid
Keywords: Compressed sensing; hemodynamics; magnetic resonance imaging (MRI); phase contrast MRI

Multidimensional noise reduction in C-arm cone-beam CT via 2D-based Landweber iteration and 3D-based deep neural networks - Choi, Dahim; Kim, Juhee; Chae, Seung Hoon; Kim, Byeongjoon; Baek, Jongduk; Maier, Andreas; Fahrig, Rebecca; Park, Hyun Seok; Choi, Jang Hwan (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019)
Recently, the necessity of using low-dose CT imaging with reduced noise has come to the forefront due to the risks involved in radiation. In order to acquire a high-resolution image from a low-resolution image which produces a relatively small amount of radiation, various algorithms including deep learning-based methods have been proposed. However, the current techniques have shown limited performance, especially with regard to losing fine details and blurring high-frequency edges. To enhance the previously suggested 2D patch-based denoising model, we have suggested the 3D block-based REDCNN model, employing convolution layers paired with deconvolution layers, shortcuts, and residual mappings. This process allows us to preserve the image structure and diagnostic features of an image, increasing image resolution by smoothing noise. Finally, we applied a bilateral filter in 3D and utilized a 2D-based Landweber iteration method to reduce remaining noise under a certain amplitude and prevent the edges from blurring. As a result, our proposed method effectively reduced Poisson noise level without losing diagnostic features and showed high performance in both qualitative and quantitative evaluation methods compared to ResNet2D, ResNet3D, REDCNN2D, and REDCNN3D.
CRIS-Team Scopus Importer:2019-07-12
Keywords: Bilateral filtering; C-arm cone-beam CT; Deep neural networks; Imaging of lower extremities; Landweber type iteration; Medical image processing; Noise reduction

Multi-level cancer profiling through joint cell-graph representations - Rivera Monroy, Luis; Rist, Leonhard; Wilm, Frauke; Ostalecki, Christian; Baur, Andreas; Vera González, Julio; Breininger, Katharina; Maier, Andreas (Smart Health, 2024)
Computer-aided analysis of digitized pathology samples has significantly advanced with the rapid progression of machine and Deep Learning (DL) methods. However, most existing approaches primarily focus on features extracted from patches due to the large image sizes. This focus limits the ability of Convolutional Neural Networks (CNNs) to capture global information from the samples, resulting in an incomplete phenotypical and topological representation and thereby restricting the diagnostic capabilities of these methods. The recent emergence of Graph Neural Networks (GNNs) offers new opportunities to overcome these limitations through graph-driven representations of pathological samples. This work introduces a graph-based framework that encompasses diverse cancer types and integrates different imaging modalities. In this framework, histopathology samples are represented as graphs, and a pipeline facilitating cell-wise and disease classification is developed. The results support this motivation: for cell-wise classification, we achieved an average accuracy of , and for disease-wise classification, an average accuracy of 83%, outperforming reference models such as XGBoost and standard CNNs. This approach not only provides flexibility in combining various diseases but also extends to integrating different staining technique
Keywords: Fluorescence microscopy imageGraph deep learningMultiplex imagingDigital pathology

Multi-Material Beam Hardening Correction (MMBHC) in Computed Tomography - Yang, Qiao; Maass, Nicole; Tian, Mengqiu; Elter, Matthias; Schasiepen, Ingo; Maier, Andreas; Hornegger, Joachim (Proceedings of the 12th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2013)

Multimodal and Multilingual Understanding of Smells using VilBERT and mUNITER - Akdemir, Kiymet; Hürriyetoğlu, Ali; Troncy, Raphaël; Paccosi, Teresa; Menini, Stefano; Zinnen, Mathias; Christlein, Vincent (CEUR Workshop Proceedings, 2023)
We evaluate state-of-the-art multimodal models to detect common olfactory references in multilingual text and images in the scope of the Multimodal Understanding of Smells in Texts and Images (MUSTI) Task at Mediaeval 2022. The goal of the MUSTI Subtask 1 is to classify pairs of text and image as to whether they refer to the same smell source or not. We approach this task as a Visual Entailment problem and evaluate the performance of the English model ViLBERT and the multilingual model mUNITER on MUSTI Subtask 1. While base VilBERT and mUNITER models perform worse than a dummy baseline, fine-tuning these models using the training data improve performance significantly in almost all scenarios. We find that fine-tuning mUNITER with SNLI-VE and MUSTI training data performs better than other configurations we implemented. Our experiments demonstrate that the task presents some challenges, but it is by no means impossible. Our code is available at https://github.com/Odeuropa/musti-eval-baselines to encourage reproducibility.
CRIS-Team Scopus Importer:2023-12-29

Multi-Modal Biomarker Extraction Framework for Therapy Monitoring of Social Anxiety and Depression Using Audio and Video - Yang, Seung Hee; Pérez Toro, Paula Andrea; Weise, Tobias; Hoffmann, Bettina; Demir, Kubilay Can; Nöth, Elmar; Kallert, Thomas; Maier, Andreas (International Conference on Machine Learning (Workshop on Machine Learning for Multimodal Healthcare), 2023)
This paper introduces a framework that can be used for feature extraction, relevant to monitoring the speech therapy progress of individuals suffering from social anxiety or depression. It operates multi-modal (decision fusion) by incorporating audio and video recordings of a patient and the corresponding interviewer, at two separate test assessment sessions. The used data is provided by an ongoing project in a day-hospital and outpatient setting in Germany, with the goal of investigating whether an established speech therapy group program for adolescents, which is implemented in a stationary and semi-stationary setting, can be successfully carried out via telemedicine. The features proposed in this multi-modal approach could form the basis for interpretation and analysis by medical experts and therapists, in addition to acquired data in the form of questionnaires. Extracted audio features focus on prosody (intonation, stress, rhythm, and timing), as well as predictions from a deep neural network model, which is inspired by the Pleasure, Arousal, Dominance (PAD) emotional model space. Video features are based on a pipeline that is designed to enable visualization of the interaction between the patient and the interviewer in terms of Facial Emotion Recognition (FER), utilizing the mini-Xception network architecture.<br

Multimodal Corpus Analysis of Autoblog 2020: Lecture Videos in Machine Learning - Hernandez, Abner; Yang, Seung Hee (Multimodal Corpus Analysis of Autoblog 2020: Lecture Videos in Machine Learning, 2021)
This paper introduces a lecture video corpus, Autoblog 2020. With the increase of online learning in universities, there is a demand for a systematic toolchain development for lecture video processing. However, the existing lecture video corpus does not satisfy the requirement for such tasks, and lecture transcription and analyses are relatively unexplored areas in speech and natural language research. Autoblog 2020 Corpus is developed towards the end goal of free video-to-blog post conversion software that supports making video presentations more accessible. It will include automatic editing of disfluencies, automatic speech recognition (ASR), and spoken term extraction so that researchers can process and share their contents more efficiently. In this paper, we present a description of the corpus, linguistic analyses and preliminary experiment results regarding ASR, keyword extraction, and segmentation. The results will be used in future work to develop a video-to-blog post conversion.<br
Keywords: Video lecture corpus; Speech recognition; Keyword extraction; Automatic segmentation; Audio-visual processing

Multi-Modal Deep Guided Filtering for Comprehensible Medical Image Processing Supplementary Material - Stimpel, Bernhard; Syben-Leisner, Christopher; Schirrmacher, Franziska; Hölter, Philip; Dörfler, Arnd; Maier, Andreas (IEEE Transactions on Medical Imaging, 2019)
EVALuna2:212806

Multi-modal Retinal Image Registration Using a Keypoint-Based Vessel Structure Aligning Network - Sindel, Aline; Hohberger, Bettina; Maier, Andreas; Christlein, Vincent (Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, 2022)
In ophthalmological imaging, multiple imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography, are often involved to make a diagnosis of retinal disease. Multi-modal retinal registration techniques can assist ophthalmologists by providing a pixel-based comparison of aligned vessel structures in images from different modalities or acquisition times. To this end, we propose an end-to-end trainable deep learning method for multi-modal retinal image registration. Our method extracts convolutional features from the vessel structure for keypoint detection and description and uses a graph neural network for feature matching. The keypoint detection and description network and graph neural network are jointly trained in a self-supervised manner using synthetic multi-modal image pairs and are guided by synthetically sampled ground truth homographies. Our method demonstrates higher registration accuracy as competing methods for our synthetic retinal dataset and generalizes well for our real macula dataset and a public fundus dataset.
Keywords: Multi-modal retinal image registration; Convolutional neural networks; Graph neural networks

Multi-Modal Super-Resolution with Deep Guided Filtering - Stimpel, Bernhard; Syben-Leisner, Christopher; Schirrmacher, Franziska; Dörfler, Arnd; Maier, Andreas; Hölter, Philip (Informatik aktuell, 2019)
Despite the visually appealing results, most Deep Learningbased super-resolution approaches lack the comprehensibility that is required for medical applications. We propose a modified version of the locally linear guided filter for the application of super-resolution in medical imaging. The guidance map itself is learned end-to-end from multimodal inputs, while the actual data is only processed with known operators. This ensures comprehensibility of the results and simplifies the implementation of guarantees. We demonstrate the possibilities of our approach based on multi-modal MR and cross-modal CT and MR data. For both datasets, our approach performs clearly better than bicubic upsampling. For projection images, we achieve SSIMs of up to 0.99, while slice image data results in SSIMs of up to 0.98 for four-fold upsampling given an image of the respective other modality at full resolution. In addition, end-to-end learning of the guidance map considerably improves the quality of the results.
CRIS-Team Scopus Importer:2019-05-14

Multi-organ Segmentation with Partially Annotated Datasets - Song, Haobo; Liu, Chang; Folle, Lukas; Maier, Andreas (Bildverarbeitung für die Medizin 2022, 2022)
Efficient and fully automatic multi-organ segmentation is of great research and clinical prospect. Deep learning (DL) based methods have recently emerged and proven its effectiveness in various biomedical segmentation tasks. The performance of DL based segmentation models strongly depends on the training dataset and a large, correctly annotated dataset is always crucial. However, gathering annotation for multi-organ segmentation task is difficult and making use of public datasets with existing annotations then becomes one possible solution. In this work we propose a pipeline for training multi-organ segmentation model from partially annotated datasets. The proposed method is evaluated using left, right lungs and liver segmentation task of throat-abdomen CT scans. From average dice score, we found the proposed method can obtain very close performance using only partially annotated datasets (0.93), compared with models using fully annotated datasets (0.96).<br

Multiple Device Segmentation for Fluoroscopic Imaging Using Multi-task Learning - Breininger, Katharina; Würfl, Tobias; Kurzendorfer, Tanja; Albarqouni, Shadi; Pfister, Marcus; Kowarschik, Markus; Navab, Nassir; Maier, Andreas (Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, 2018)
For endovascular aortic repair (EVAR), integrating preoperative information of the aortic anatomy with intraoperative fluoroscopy can aid in reducing radiation exposure, contrast agent and procedure time. However, the quality of this fusion may deteriorate over the course of the intervention due to patient movement or deformation of the vasculature caused by interventional tools. Automatically detecting the instruments present in the X-ray image can help to assess the degree of deterioration, trigger automatic re-registration or aid in automatic workflow phase detection and process modeling. In this work, we investigate a flexible approach to segment different devices based on fully convolutional neural networks using multi-task learning. We evaluate the proposed approach on a set of 38 X-ray images acquired during EVAR interventions by targeting the segmentation of aortic stents, stiff guidewires and pigtail catheters. We compare the results to the performance of single-task networks. We manage to keep similar performance compared to single-task networks with Dice coefficients between 0.95 and 0.80 depending on the device, while speeding up computation by a factor of two.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.multip
Keywords: Catheters; Convolutional Neural Networks; Endovascular Aortic Repair; Guidewires; Multi-task learning; Stents; X-ray

Multiple Source Translation Micro-CT Improves Both Field-of-View and Spatial Resolution - Yu, Haijun; Ni, Song; Thies, Mareike; Wagner, Fabian; Huang, Yixing; Liu, Fenglin; Maier, Andreas (IEEE Transactions on Instrumentation and Measurement, 2024)
Object rotation is a classical data acquisition mode in micro-computed tomography (micro-CT) to capture radiographs from different angles for high-resolution three-dimensional reconstruction. However, this mode encounters two primary limitations: firstly, an undesired tradeoff arises between the field-of-view (FOV) and the spatial resolution; secondly, the spatial resolution is compromised due to insufficient radial sampling in the frequency domain. In this paper, we present a novel acquisition paradigm called multiple Source Translations micro-CT (mSTCT) to simultaneously improve FOV and spatial resolution. In mSTCT, the FOV is enlarged by extending the source translation distance and the spatial resolution is enhanced by refining the source sample spacing to increase the radial sampling in the frequency domain. Theoretically, mSTCT can enhance the FOV and spatial resolution to their physical limits. We formulate a central slice theorem for mSTCT to elucidate its fundamental principle. Subsequently, we derive a corresponding analytical algorithm without projection rebinning or stitching for reconstruction. The physical experiments conducted in this work validate the efficacy of mSTCT in terms of FOV extension and spatial resolution enhancement. As mSTCT is trivial to implement, it holds considerable promise for diverse scenarios demanding high-resolution and large FOV in Non-Destructive Testing (NDT) applications.
CRIS-Team Scopus Importer:2024-04-19
Keywords: Data sampling; field-of-view (FOV); micro-computed tomography (micro-CT); scanning mode; spatial resolution.

Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans - Ghesu, Florin-Cristian; Georgescu, Bogdan; Zheng, Yefeng; Grbic, Sasa; Maier, Andreas; Hornegger, Joachim; Comaniciu, Dorin (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019)
Robust and fast detection of anatomical structures is a prerequisite for both diagnostic and interventional medical image analysis. Current solutions for anatomy detection are typically based on machine learning techniques that exploit large annotated image databases in order to learn the appearance of the captured anatomy. These solutions are subject to several limitations, including the use of suboptimal feature engineering techniques and most importantly the use of computationally suboptimal search-schemes for anatomy detection. To address these issues, we propose a method that follows a new paradigm by reformulating the detection problem as a behavior learning task for an artificial agent. We couple the modeling of the anatomy appearance and the object search in a unified behavioral framework, using the capabilities of deep reinforcement learning and multi-scale image analysis. In other words, an artificial agent is trained not only to distinguish the target anatomical object from the rest of the body but also how to find the object by learning and following an optimal navigation path to the target object in the imaged volumetric space. We evaluated our approach on 1487 3D-CT volumes from 532 patients, totaling over 500,000 image slices and show that it significantly outperforms state-of-the-art solutions on detecting several anatomical structures with no failed cases from a clinical acceptance perspective, while also achieving a 20-30 percent higher detection accuracy. Most importantly, we improve the detection-speed of the reference methods by 2-3 orders of magnitude, achieving unmatched real-time performance on large 3D-CT scans.
CRIS-Team Scopus Importer:2019-06-07
Keywords: Deep learning; deep reinforcement learning; intelligent localization; medical image analysis; multi-scale; real-time detection; scale-space modeling; three-dimensional (3D) object detection

Multi-scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset - Wilm, Frauke; Fragoso, Marco; Bertram, Christof A.; Stathonikos, Nikolas; Öttl, Mathias; Qiu, Jingna; Klopfleisch, Robert; Maier, Andreas; Breininger, Katharina; Aubreville, Marc (Informatik aktuell, 2023)
In histopathology, scanner-induced domain shifts are known to impede the performance of trained neural networks when tested on unseen data. Multidomain pre-training or dedicated domain-generalization techniques can help to develop domain-agnostic algorithms. For this, multi-scanner datasets with a high variety of slide scanning systems are highly desirable. We present a publicly available multi-scanner dataset of canine cutaneous squamous cell carcinoma histopathology images, composed of 44 samples digitized with five slide scanners. This dataset provides local correspondences between images and thereby isolates the scanner-induced domain shift from other inherent, e.g. morphology-induced domain shifts. To highlight scanner differences, we present a detailed evaluation of color distributions, sharpness, and contrast of the individual scanner subsets. Additionally, to quantify the inherent scanner-induced domain shift, we train a tumor segmentation network on each scanner subset and evaluate the performance both in - and cross-domain. We achieve a class-averaged in-domain intersection over union coefficient of up to 0.86 and observe a cross-domain performance decrease of up to 0.38, which confirms the inherent domain shift of the presented dataset and its negative impact on the performance of deep neural networks.
Created from Fastlane, Scopus look-up

Multi-stage Fine-Tuning Deep Learning Models Improves Automatic Assessment of the Rey-Osterrieth Complex Figure Test - Schuster, Benjamin; Kordon, Florian Johannes; Mayr, Martin; Seuret, Mathias; Jost, Stefanie; Kessler, Josef; Christlein, Vincent (Document Analysis and Recognition - ICDAR 2023, 2023)
The Rey-Osterrieth Complex Figure Test (ROCFT) is a widely used neuropsychological tool for assessing the presence and severity of different diseases. It involves presenting a complex illustration to the patient who is asked to copy it, followed by recall from memory after 3 and 30 min. In clinical practice, a human rater evaluates each component of the reproduction, with the overall score indicating illness severity. However, this method is both time-consuming and error-prone. Efforts have been made to automate the process, but current algorithms require large-scale private datasets of up to 20,000 illustrations. With limited data, training a deep learning model is challenging. This study addresses this challenge by developing a fine-tuning strategy with multiple stages. We show that pre-training on a large-scale sketch dataset with initialized weights from ImageNet significantly reduces the mean absolute error (MAE) compared to just training with initialized weights from ImageNet, e.g., ReXNet-200 from 3.1 to 2.2 MAE. Additionally, techniques such as stochastic weight averaging (SWA) and ensembling of different architectures can further reduce the error to an MAE of 1.97.<br
Keywords: Rey-Osterrieth Complex Figure; Regression; Sketch pre-training; Ensembling

Multi-Stage Platform for (Semi-)Automatic Planning in Reconstructive Orthopedic Surgery - Kordon, Florian Johannes; Maier, Andreas; Swartman, Benedict; Privalov, Maxim; El Barbari, Jan Siad; Kunze, Holger (Journal of Imaging, 2022)
Intricate lesions of the musculoskeletal system require reconstructive orthopedic surgery to restore the correct biomechanics. Careful pre-operative planning of the surgical steps on 2D image data is an essential tool to increase the precision and safety of these operations. However, the plan’s effectiveness in the intra-operative workflow is challenged by unpredictable patient and device positioning and complex registration protocols. Here, we develop and analyze a multi-stage algorithm that combines deep learning-based anatomical feature detection and geometric post-processing to enable accurate pre- and intra-operative surgery planning on 2D X-ray images. The algorithm allows granular control over each element of the planning geometry, enabling real-time adjustments directly in the operating room (OR). In the method evaluation of three ligament reconstruction tasks effect on the knee joint, we found high spatial precision in drilling point localization (ε<2.9mm" role="presentation" style="max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">ε<2.9mm) and low angulation errors for k-wire instrumentation (ε<0.75∘" role="presentation" style="max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">ε<0.75∘) on 38 diagnostic radiographs. Comparable precision was demonstrated in 15 complex intra-operative trauma cases suffering from strong implant overlap and multi-anatomy exposure. Furthermore, we found that the diverse feature detection tasks can be efficiently solved with a multi-task network topology, improving precision over the single-task case. Our platform will help overcome the limitations of current clinical practice and foster surgical plan generation and adjustment directly in the OR, ultimately motivating the development of novel 2D planning guidelines.<br
Keywords: computer-assisted surgery; surgical planning; reconstructive orthopedic surgery; ligament reconstruction; deep learning; multi-task learning; X-ray images

Multi-Task Framework for X-Ray Guided Planning in Knee Surgery - Kordon, Florian Johannes; Fischer, Peter; Privalov, Maxim; Swartman, Benedict; Schnetzke, Marc; Franke, Jochen; Lasowski, Ruxandra; Maier, Andreas; Kunze, Holger (Bildverarbeitung für die Medizin 2020, 2020)
Keywords: Landmark Localization; Multi-label Bone Segmentation; MPFL X-Ray Guidance; Orthopaedics; Surgical Planning

Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels - Gündel, Sebastian; Ghesu, Florin-Cristian; Grbic, Sasa; Gibson, Eli; Georgescu, Bogdan; Maier, Andreas; Comaniciu, Dorin (2019)

Multi-task Localization and Segmentation for X-Ray Guided Planning in Knee Surgery - Kordon, Florian Johannes; Fischer, Peter; Privalov, Maxim; Swartman, Benedict; Schnetzke, Marc; Franke, Jochen; Lasowski, Ruxandra; Maier, Andreas; Kunze, Holger (Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, 2019)
X-ray based measurement and guidance are commonly used tools in orthopaedic surgery to facilitate a minimally invasive workflow. Typically, a surgical planning is first performed using knowledge of bone morphology and anatomical landmarks. Information about bone location then serves as a prior for registration during overlay of the planning on intra-operative X-ray images. Performing these steps manually however is prone to intra-rater/inter-rater variability and increases task complexity for the surgeon. To remedy these issues, we propose an automatic framework for planning and subsequent overlay. We evaluate it on the example of femoral drill site planning for medial patellofemoral ligament reconstruction surgery. A deep multi-task stacked hourglass network is trained on 149 conventional lateral X-ray images to jointly localize two femoral landmarks, to predict a region of interest for the posterior femoral cortex tangent line, and to perform semantic segmentation of the femur, patella, tibia, and fibula with adaptive task complexity weighting. On 38 clinical test images the framework achieves a median localization error of 1.50 mm for the femoral drill site and mean IOU scores of 0.99, 0.97, 0.98, and 0.96 for the femur, patella, tibia, and fibula respectively. The demonstrated approach consistently performs surgical planning at expert-level precision without the need for manual correctio
Keywords: Landmark localization; Multi-label bone segmentation; MPFL X-ray guidance; Orthopaedics; Surgical planning

Multi-View Depth-Aware Rigid 2-D/3-D Registration - Schaffert, Roman; Wang, Jian; Fischer, Peter; Borsdorf, Anja; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
UnivIS-Import:2018-02-22:Pub.2017.tech.IMMD.IMMD5.multiv

MUSTI - Multimodal Understanding of Smells in Texts and Images at MediaEval 2022 - Hürriyetoğlu, Ali; Paccosi, Teresa; Menini, Stefano; Zinnen, Mathias; Lisena, Pasquale; Akdemir, Kiymet; Troncy, Raphaël; van Erp, Marieke (CEUR Workshop Proceedings, 2023)
MUSTI aims to collect information about smell from digital text and image collections from the 17th to 20th century in a multilingual setting. More precisely, MUSTI studies the relatedness of evocation of smells (smell sources being identified, objects being detected, gestures being mentioned or recognized) between texts and images. The main task is a binary classification task and entails identifying whether a pair of image and a text snippet contains the same smell source independent of what is the smell source. An optional sub-task is the determination of the smell sources that make the respective pair related.
CRIS-Team Scopus Importer:2023-12-29

Myocardial Scar Segmentation in LGE-MRI using Fractal Analysis and Random Forest Classification - Kurzendorfer, Tanja; Breininger, Katharina; Steidl, Stefan; Brost, Alexander; Forman, Christoph; Maier, Andreas (Proceedings - International Conference on Pattern Recognition, 2018)
Late-gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the clinical gold standard to visualize myocardial scarring. The gadolinium based contrast agent accumulates in the damaged cells and leads to various enhancements in the LGE-MRI scan. The quantification of the scar tissue is very important for diagnosis, treatment planning, and guidance during the procedure. In clinical routine, the scar is often segmented manually. However, manual segmentation is prone to inter- and intra-observer variability and very time consuming. In this work a new texture based scar quantification is proposed. For texture characterization, segmentation based fractal analysis is used. First, the image is decomposed into a set of binary images by applying a two-threshold binary decomposition. Second, a set of features are extracted for each of the binary images, namely the fractal dimension, the mean gray value, and the size of the binary object. In addition, the local and global intensity of each patch is added to the feature vector. In the next step, the features are classified using a random forest classifier. The scar quantification is evaluated on 30 clinical LGE-MRI data sets. In addition, the results are compared to the x-fold standard deviation approach and the full-width-at-half-max method, which are implemented in a fully automatic manner. The proposed scar quantification achieved a mean Dice coefficient of 0.64±0.17 and outperforms the x-fold standard deviation approach.
CRIS-Team Scopus Importer:2020-11-20

Myocardial scar segmentation on cardiac LGE images with reduced contrast agent dose using deep learning - Vornehm, Marc; Fenski, Maximilian; Preuhs, Elisabeth; Maier, Andreas; Schulz-Menger, Jeanette; Wetzl, Jens; Giese, Daniel (Abstractband der 24. Jahrestagung der Deutschen Sektion der ISMRM, 2022)

Myocardial Twist from X-ray Angiography - Geimer, Tobias; Unberath, Mathias; Höhn, Johannes; Achenbach, Stephan; Maier, Andreas (Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme - Anwendungen, 2018)
We present preliminary evidence that left ventricular twist can be observed and thus estimated from rotational coronary angiography. Our method is based on an ellipsoidal parametric model initially developed for functional analysis of cardiac tagged MRI. First, we fit the model to 3D coronary artery centerlines reconstructed from rotational angiography and then use 3D/2D registration to optimize for the functional parameters driving the model. On two clinical acquisitions, we show that our method is able to recover cardiac motion indicated by an average reduction in reprojection error of 28.1±3.0 %. Analysis of the functional progression of the functional parameters over time reveals radial and longitudinal contraction, and left ventricular twist. We believe that these results are exciting and encourage improvement of the proposed method in future work.<br
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.myocar

Myocardial twist from X-ray angiography: Can we observe left ventricular twist in rotational coronary angiography? - Geimer, Tobias; Unberath, Mathias; Höhn, Johannes; Achenbach, Stephan; Maier, Andreas (Informatik aktuell, 2018)
We present preliminary evidence that left ventricular twist can be observed and thus estimated from rotational coronary angiography. Our method is based on an ellipsoidal parametric model initially developed for functional analysis of cardiac tagged MRI. First, we fit the model to 3D coronary artery centerlines reconstructed from rotational angiography and then use 3D/2D registration to optimize for the functional parameters driving the model. On two clinical acquisitions, we show that our method is able to recover cardiac motion indicated by an average reduction in reprojection error of 28.1±3.0%. Analysis of the functional progression of the functional parameters over time reveals radial and longitudinal contraction, and left ventricular twist. We believe that these results are exciting and encourage improvement of the proposed method in future work.
CRIS-Team Scopus Importer:2022-06-05

NEGATIVE VESSEL REMODELING IN STARGARDT DISEASE QUANTIFIED WITH VOLUME-RENDERED OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY - Reich, Michael; Dreesbach, Michelle; Boehringer, Daniel; Schottenhamml, Julia; Gehring, Esteban; Scholl, Hendrik P.N.; Inglin, Nadja; Agostini, Hansjuergen; Reinhard, Thomas; Lagrèze, Wolf A.; Spaide, Richard F.; Lange, Clemens; et al. (Retina (Philadelphia, Pa.), 2021)
PURPOSE: To quantify retinal vasculature changes in Stargardt disease1 (STGD1) with volume-rendered optical coherence tomography angiography. METHODS: Optical coherence tomography angiography volumes from healthy subjects and two subgroups of patients with STGD1 with the presence/absence of definitely decreased autofluorescence areas were compared. Optical coherence tomography angiography vessel surface area and vessel volume were measured in central zones (Z) of 1-, 2-, and 3-mm diameter. RESULTS: Twenty nine eyes of 15 patients with STGD1 (20/9 eyes with/without definitely decreased autofluorescence) and 30 eyes of 15 controls contributed data. An enlarged foveal avascular zone was found in patients with STGD1 without and even more with definitely decreased autofluorescence associated with a vessel rarefication in central and also paracentral zones with unnoticeable autofluorescence. Vessel surface area and vessel volume were reduced in both STGD1 subgroups for all zones (P < 0.0001). Stargardt disease 1 eyes when compared to without definitely decreased autofluorescence showed reduced vessel surface area and vessel volume in Z2+3 (both P < 0.05). CONCLUSION: Volume rendering of optical coherence tomography angiography in STGD1 shows a reduced retinal flow in the central macula. This is most likely secondary to loss of neurosensory tissue with disease progression and therefore not likely be favorably influenced by gene transfer and retinal pigment epithelial transplantation. Retinal blood flow assessed by 3D volume-rendered optical coherence tomography angiography could serve as surrogate marker for vascular changes of the central retina.
CRIS-Team Scopus Importer:2021-09-24

Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model - Haskell, Melissa; Cauley, Stephen; Belgic, Berkin; Hoßbach, Julian; Splitthoff, Daniel; Pfeuffer, Josef; Setsompop, Kawin; Wald, Lawrence (Magnetic Resonance in Medicine, 2019)
Keywords: convolutional neural networks; machine learning; magnetic resonance; deep learning; image reconstruction; motion correction

Neural network based formation of cognitive maps of semantic spaces and the putative emergence of abstract concepts - Stöwer, Paul; Schilling, Achim; Maier, Andreas; Krauß, Patrick (Scientific Reports, 2023)

Neural network-based fully automated cardiac resting phase detection algorithm compared with manual detection in patients - Ogawa, Ryo; Kido, Tomoyuki; Shiraishi, Yasuhiro; Yagi, Yuri; Yoon, Seung Su; Wetzl, Jens; Schmidt, Michaela; Kido, Teruhito (Acta Radiologica Open, 2022)
Background: A cardiac resting phase is used when performing free-breathing cardiac magnetic resonance examinations.
CRIS-Team WoS Importer:2022-11-18

Neural network based successor representations to form cognitive maps - Stöwer, Paul; Krauß, Patrick; Maier, Andreas (Bernstein Conference, 2022)

Neural network based successor representations to form cognitive maps of space and language - Stöwer, Paul; Schliecker, Christian; Schilling, Achim; Metzner, Claus; Maier, Andreas; Krauß, Patrick (Scientific Reports, 2022)

Neural Networks for Distinguishing Rheumatoid Arthritis from Psoriatic Arthritis by Using Magnetic Resonance Imaging - Folle, Lukas; Bayat, Sara; Kleyer, Arnd; Fagni, Filippo; Kapsner, Lorenz; Schlereth, Maja; Meinderink, Timo; Breininger, Katharina; Tascilar, Koray; Krönke, Gerhard; Uder, Michael; Sticherling, Michael; et al. (Arthritis Rheumatol., 2022)

Neural Networks with Fixed Binary Random Projections Improve Accuracy in Classifying Noisy Data - Yang, Zijin; Schilling, Achim; Maier, Andreas; Krauß, Patrick (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

New Approaches to Dose Reduction in Interventional CT Imaging - Maier, Andreas (International Hospital, 2013)
UnivIS-Import:2016-02-10:Pub.2013.tech.IMMD.IMMD5.newapp

New Approaches to Dose Reduction in Interventional CT Imaging - Maier, Andreas (2013)

New Approaches to OCR for Early Printed Books - Weichselbaumer, Nikolaus; Seuret, Mathias; Limbach, Saskia; Dong, Rui; Burghardt, Manuel; Christlein, Vincent (DigItalia, 2020)

Next-generation imaging of the skeletal system and its blood supply - Grüneboom, Anika; Kling, Lasse; Christiansen, Silke; Mill, Leonid; Maier, Andreas; Engelke, Klaus; Quick, Harald H.; Schett, Georg; Gunzer, Matthias (Nature Reviews Rheumatology, 2019)
Bone is organized in a hierarchical 3D architecture. Traditionally, analysis of the skeletal system was based on bone mass assessment by radiographic methods or on the examination of bone structure by 2D histological sections. Advanced imaging technologies and big data analysis now enable the unprecedented examination of bone and provide new insights into its 3D macrostructure and microstructure. These technologies comprise ex vivo and in vivo methods including high-resolution computed tomography (CT), synchrotron-based imaging, X-ray microscopy, ultra-high-field magnetic resonance imaging (MRI), light-sheet fluorescence microscopy, confocal and intravital two-photon imaging. In concert, these techniques have been used to detect and quantify a novel vascular system of trans-cortical vessels in bone. Furthermore, structures such as the lacunar network, which harbours and connects osteocytes, become accessible for 3D imaging and quantification using these methods. Next-generation imaging of the skeletal system and its blood supply are anticipated to contribute to an entirely new understanding of bone tissue composition and function, from macroscale to nanoscale, in health and disease. These insights could provide the basis for early detection and precision-type intervention of bone disorders in the future.
CRIS-Team Scopus Importer:2019-08-27

Noise2Contrast: Multi-contrast Fusion Enables Self-supervised Tomographic Image Denoising - Wagner, Fabian; Thies, Mareike; Pfaff, Laura; Maul, Noah; Pechmann, Sabrina; Gu, Mingxuan; Utz, Jonas; Aust, Oliver; Weidner, Daniela; Neag, Georgiana; Uderhardt, Stefan; Choi, Jang Hwan; et al. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usually optimize loss metrics that are calculated from multiple noisy realizations of similar images, e.g., from neighboring tomographic slices. However, those approaches fail to utilize the multiple contrasts that are routinely acquired in medical imaging modalities like MRI or dual-energy CT. In this work, we propose the new self-supervised training scheme Noise2Contrast that combines information from multiple measured image contrasts to train a denoising model. We stack denoising with domain-transfer operators to utilize the independent noise realizations of different image contrasts to derive a self-supervised loss. The trained denoising operator achieves convincing quantitative and qualitative results, outperforming state-of-the-art self-supervised methods by 4.7–11.0%/4.8–7.3% (PSNR/SSIM) on brain MRI data and by 43.6–50.5%/57.1–77.1% (PSNR/SSIM) on dual-energy CT X-ray microscopy data with respect to the noisy baseline. Our experiments on different real measured data sets indicate that Noise2Contrast training generalizes to other multi-contrast imaging modalities.
CRIS-Team Scopus Importer:2023-07-28
Keywords: Contrast Fusion; Known Operator Learning; Self-Supervised Denoising

Noise Reduction In Low-Dose CT Using a 3D Multiscale Sparse Denoising Autoencoder - Mentl, Katrin; Mailhé, Boris; Ghesu, Florin-Cristian; Schebesch, Frank; Haderlein, Tino; Maier, Andreas; Nadar, Mariappan (2017 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.noiser_1

Noise reduction in optical coherence tomography images using a deep neural network with perceptually-sensitive loss function - Qiu, Bin; Huang, Zhiyu; Liu, Xi; Meng, Xiangxi; You, Yunfei; Liu, Gangjun; Yang, Kun; Maier, Andreas; Ren, Qiushi; Lu, Yanye (Biomedical Optics Express, 2020)
Optical coherence tomography (OCT) is susceptible to the coherent noise, which is the speckle noise that deteriorates contrast and the detail structural information of OCT images, thus imposing significant limitations on the diagnostic capability of OCT. In this paper, we propose a novel OCT image denoising method by using an end-to-end deep learning network with a perceptually-sensitive loss function. The method has been validated on OCT images acquired from healthy volunteers’ eyes. The label images for training and evaluating OCT denoising deep learning models are images generated by averaging 50 frames of respective registered B-scans acquired from a region with scans occurring in one direction. The results showed that the new approach can outperform other related denoising methods on the aspects of preserving detail structure information of retinal layers and improving the perceptual metrics in the human visual perception.
CRIS-Team Scopus Importer:2020-02-14

Noise reduction method in low-dose CT data combining neural networks and an iterative reconstruction technique - Choi, Dahim; Kim, Juhee; Chae, Seung Hoon; Baek, Jongduk; Maier, Andreas; Fahrig, Rebecca; Park, Hyun Seok; Choi, Jang Hwan (Proceedings of SPIE - The International Society for Optical Engineering, 2019)
Improving image quality from low-dose CT image and keeping diagnostic features is integral to lowering the amount of exposure to radiation and its potential risks. Noise reduction methods using deep neural network have been developed and displayed impressive performance, but there are limitations on noise remnants, blurring on high-frequency edge, and artifacts occurrence. To increase noise reduction performance and deal with those issues simultaneously, we have implemented block-based REDCNN model and applied patch-based Landweber-type iteration to images passed through REDCNN model. The model successfully smooths noise on CT images which are imposed Gaussian and Poisson noise, and outperforms noise reduction by other state-of-the-art deep neural network models. We also have tested the effect of repetition of an iterative reconstruction, changing a step size and the number of iteration.
CRIS-Team Scopus Importer:2019-05-02
Keywords: Deep Neural Networks; Landweber iteration; Low-dose CT; Noise Reduction; REDCNN

Non-Contrast Head CT Imaging Using Dual Energy: A Study on Bias Induced by Imperfections in Energy Response and Material Knowledge - Haase, Viktor; Stierstorfer, Karl; Hahn, Katharina; Schoendube, Harald; Maier, Andreas; Noo, Frédéric (Proceedings of the 16th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2021)
Diagnostic of stroke and proton therapy treatment planning require accurate non-contrast head CT imaging. Dual energy CT offers improvements in this field by enabling material decomposition and virtual mono-energetic images. Such a computation in the projection space requires an energy response model for the CT system and a selection of materials for data decomposition. In this work, we use incorrect response models and materials to study the effects of imperfections on the quality of the material decomposition. The experiments are carried out in fanbeam geometry with noise-free computer simulated data of two head phantoms. The results show that small errors in the energy response can quickly lead to small inhomogeneity and to beam hardening errors between close bones. They also show that the difference between soft tissues and water lead to soft tissue leakage in the bone image, but may not have a negative effect on the mono-energetic image.<br
Keywords: Dual Energy Computed Tomography; Material Decomposition; Energy Response Model; Non-Contrast Head CT Imaging; Simulation Study

Non-Destructive Digitization of Soiled Historical Chinese Bamboo Scrolls - Stromer, Daniel; Christlein, Vincent; Huang, Xiaolin; Zippert, Patrick; Helmecke, Eric; Hausotte, Tino; Maier, Andreas (2018 13th IAPR Workshop on Document Analysis Systems (DAS), 2018)
For about 2000 years, no paper was used as a media in China but writings and drawings were captured on bamboo and wooden slips. Several slips were bound together with strips and rolled up to a scroll. The writings and drawings were either brushed or even carved into the wood. Those documents are very precious for culture inheritance and research, but due to aging processes, the discovered pieces are sometimes in a poor condition and also soiled. Because cleaning the slips is not only challenging but also writings could be erased, we developed a method to digitize such historical documents without the need of cleaning. We perform a 3-D X-ray micro-CT scan resulting in a 3-D volume of the complete document. With our approach, we were able to investigate the scroll without any manual labor (e.g. unwrapping or cleaning). We showed that the method also works for heavily soiled scrolls where nothing is readable with the naked eye. This can help conservators to store all writings before they may be erased by the cleaning process. Finally, we present a manual technique to virtually unwrap and post-process the documents resulting in a 2-D image of all bamboo slips.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.nondes
Keywords: Bamboo and wooden slips; 3-D X-ray CT; Historical Chinese Documents; Document Digitization

Non-deterministic Behavior of Ranking-Based Metrics When Evaluating Embeddings - Nicolaou, Anguelos; Dey, Sounak; Christlein, Vincent; Maier, Andreas; Karatzas, Dimosthenis (RRPR 2018: Reproducible Research in Pattern Recognition, 2019)
Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits.
CRIS-Team Scopus Importer:2019-07-23
Keywords: Adversarial; Deterministic; mAP; Performance evaluation; Precision; Ranking; Recall; Word spotting

Non-invasive Imaging Methods for Digital Humanities, Medicine, and Quality Assessment - Stromer, Daniel (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Keywords: Book CT; Digital humanities; Non-invasive imaging; Optical coherence tomography; Quality assessment; Scroll CT; Segmentation

Normalization techniques for CNN based analysis of surgical cone beam CT volumes - Martín Vicario, Celia; Kordon, Florian Johannes; Denzinger, Felix; El Barbari, Jan Siad; Privalov, Maxim; Franke, Jochen; Maier, Andreas; Kunze, Holger (Medial Imaging 2022, Image Processing, 2022)
Purpose: The choice of input normalization has effects on the generalization and performance of deep neural networks. While this topic is explored for 2D imaging applications, the influence of different normalization techniques on medical imaging modalities, e.g. cone-beam CT (CBCT), differs due to a different value range and distribution. In this paper a good normalization technique for intra-operatively acquired surgical CBCT volumes is presented. Methods: A set of normalization strategies, namely histogram equalization, min-max scaling, z-score normalization, linear look up table (LUT) with clipping and sigmoid function with clipping is compared on a CBCT volume classification task. Results: The results show that a combination of parameterized LUTs and clipping with the range [-710, 1640] HU independent of the underlying intensity histogram provides the best performance for the task at hand. Conclusions: The clipping based normalization technique helps to compress the feature space to the relevant range. By this approach, most of the information about the intensity values of soft tissue and bone is retained. The clipping range presented in this paper is valid for surgical CBCTs.<br
Keywords: Cone-beam CT; Normalization

Novel biomarker of sphericity and cylindricity indices in volume-rendering optical coherence tomography angiography in normal and diabetic eyes: a preliminary study - Maloca, Peter M.; Spaide, Richard F.; de Carvalho, Emanuel Ramos; Studer, Harald P.; W Hasler, Pascal; Scholl, Hendrik P.N.; Heeren, Tjebo F.C.; Schottenhamml, Julia; Balaskas, Konstantinos; Tufail, Adnan; Egan, Catherine (Graefes Archive For Clinical and Experimental Ophthalmology, 2020)
Purpose: Preliminary to evaluate geometric indices (vessel sphericity and cylindricity) for volume-rendered optical coherence tomography angiography (OCTA) in healthy and diabetic eyes. Methods: Twenty-six eyes of 13 healthy subjects and 12 eyes of patients with central ischemic, non-proliferative diabetic retinopathy were included. OCTA volume and surface area of the foveal vessels were measured and compared to determine OCTA sphericity and cylindricity indices and surface efficiency (SE). Results: The overall average OCTA volume in healthy was 0.49 ± 0.09 mm3 (standard deviation [SD]), compared to 0.44 ± 0.07 mm3 (SD) in the diabetic eyes (difference in means 0.06 mm3, p = 0.054). The overall average OCTA surface area in the healthy eyes was 87.731 ± 9.51 mm2 (SD), compared to 76.65 ± 13.67 mm2 (SD) in the diabetic eyes (difference in means 11.08 mm2, p = 0.021). In relation to total foveolar tissue volume, the proportion of blood vessels was 22% in healthy individuals and only 20% in diabetics. The difference between the groups was more pronounced with respect to the total OCTA surface area, with a decrease of 13% in diabetics. A diabetic eye was most likely using geometric vessel indices analysis if the sphericity value was ≥ 0.190, with a cylindricity factor of ≥ 0.001. Reproducibility of the method was good. Conclusions: A method for OCTA surface area and volume measurements was developed. The application of the novel OCTA sphericity and cylindricity indices could be suitable as temporal biomarker to characterize stable disease or disease progression and may contribute to a better understanding in the evolution of diabetic retinopathy.
CRIS-Team Scopus Importer:2020-01-21
Keywords: Cylindricity; Diabetes; Optical coherence tomography angiography; Retina; Sphericity; Volume-rendering

Novel Evaluation Metrics for Vascular Structure Segmentation - Reimann, Marcel; Fu, Weilin; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

Novel robust stability criteria for uncertain parameter quaternionic neural networks with mixed delays: Whole quaternionic method - Pan, Jie; Pan, Zhaoya (Applied Mathematics and Computation, 2021)
This paper concentrates on the robust stability of uncertain parameter quaternionic neural networks (QNNs) with both time-varying delays and infinite distributed delays. To this end, a derivative formula of quaternionic function's norm is firstly established. Then, based on this formula, algebraic standards are obtained by employing M-matrix theory as well as analytical techniques to guarantee the global robust exponential stability of the considered QNNs. Particularly, different from most existing decomposition approaches, this whole quaternionic method can be used whether the QNNs are decomposable or not and greatly reduces computation cost. The utility of the easy-to-use results formulated in the form of quaternionic norm's M-matrix is confirmed by three given instances with numerical simulation.
CRIS-Team Scopus Importer:2021-05-21
Keywords: Global robust exponential stability; M-Matrix; Mixed delay; Quaternionic neural networks; Uncertain parameter

Novel technologies for mitigation of cone-beam artifacts in C-arm CT imaging of the head - Guo, Zijia (Proceedings of Fully3D'17, 2017)
UnivIS-Import:2019-08-15:Pub.2017.tech.IMMD.IMMD5.novelt_5

Numeric quantification of intelligibility in schoolchildren with isolated and combined cleft palate - Vogt, Beate; Maier, Andreas; Batliner, Anton; Nöth, Elmar; Nkenke, Emeka; Eysholdt, Ulrich; Schuster, Maria (HNO, 2007)
Objective. Even after adequate surgical and nonsurgical treatment, combined or isolated clefts often cause functional disorders, such as speech disorders. Speech disorders vary widely in extent and can take the form, for example, of specific features of articulation, with altered nasal emission and shifted articulation, leading to reduced speech intelligibility. So far it has not been possible to describe the relationship between cleft type and intelligibility except subjective, categorical evaluation. Methods. Intelligibility of the speech as influenced by cleft palate in 58 children aged 9±2 was quantified objectively and numerically by means of automatic speech recognition technology in speech recordings for the PLAKSS test [test for psycholinguistic analysis of childhood speech impairments]. It was found that 33 children had a unilateral cleft lip and palate (CLP), 9, bilateral CLP, 10, isolated cleft palate, and 6, submucosal cleft palate. As a control group, 84 children aged 9±1 years and with physiological articulation were recruited from a mainstream school. Speech intelligibility is expressed as word accuracy, which means the percentage of correctly recognized words in a word sequence. Results. Word accuracy levels in the control group were between 39% and 84% (mean 62%±10%), while the children with clefts achieved values between 0% and 73% (mean 42%±17%). There was no difference in intelligibility between children with isolated cleft palate and those with combined cleft type. Differences were, however, found between each group and the control group (p<0.01). Conclusion. Speech intelligibility of children with isolated cleft palate does not significantly differ from that of children with combined cleft type including the palate, as has been objectively quantified for the first time by objective methods. In the presence of speech disorders, the need for diagnosis and therapy is just as great in children with isolated cleft palate as in children with combined cleft types. © 2007 Springer Medizin Verlag.
Keywords: Automatic speech recognition; Cleft lip and palate; Isolated cleft palate; Objective evaluation; Speech intelligibility

Numerische Quantifizierung der Verständlichkeit von Schulkindern mit isolierter und kombinierter Gaumenspalte - Vogt, Beate; Maier, Andreas; Batliner, Anton; Nöth, Elmar; Nkenke, Emeka; Eysholdt, Ulrich; Schuster, Maria (HNO, 2007)

Nyquist Plot Parametrization for Quantitative Analysis of Vibration of the Vocal Folds - Arias Vergara, Tomás; Döllinger, Michael; Schraut, Tobias; Mohd Khairuddin, Khairy Anuar; Schützenberger, Anne (Journal of Voice, 2023)
Objectives: The Nyquist plot provides a graphical representation of the glottal cycles as elliptical trajectories in a 2D plane. This study proposes a methodology to parameterize the Nyquist plot with application to support the quantitative analysis of voice disorders. Methods: We considered high-speed videoendoscopy recordings of 33 functional dysphonia (FD) patients and 33 normophonic controls (NC). Quantitative analysis was performed by computing four shape-based parameters from the Nyquist plot: Variability, Size (Perimeter and Area), and Consistency. Additionally, we performed automatic classification using a linear support vector machine and feature importance analysis by combining the proposed features with state-of-the-art glottal area waveform (GAW) parameters. Results: We found that the inter-cycle variability was significantly higher in FD patients compared to NC. We achieved a classification accuracy of 83% when the top 30 most important features were used. Furthermore, the proposed Nyquist plot features were ranked in the top 12 most important features. Conclusions: The Nyquist plot provides complementary information for subjective and objective assessment of voice disorders. On the one hand, with visual inspection it is possible to observe intra- and inter-glottal cycle irregularities during sustained phonation. On the other hand, shaped-based parameters allow quantifying such irregularities and provide complementary information to state-of-the-art GAW parameters.
CRIS-Team Scopus Importer:2023-03-03
Keywords: Functional dysphonia; High-speed videoendoscopy; Laryngeal imaging; Machine learning; Nyquist plot; Voice disorders

Objective vs. Subjective Evaluation of Speakers with and without Complete Dentures - Haderlein, Tino; Bocklet, Tobias; Maier, Andreas; Nöth, Elmar; Knipfer, Christian; Stelzle, Florian (Proc. Text, Speech and Dialogue; 12th International Conference, 2009)

Object Removal in Gradient Domain of Cone-Beam CT Projections - Bier, Bastian; Berger, Martin; Maier, Jennifer; Unberath, Mathias; Hsieh, Scott; Bonaretti, Serena; Fahrig, Rebecca; Levenston, Marc; Gold, Garry; Maier, Andreas (2016 IEEE Nuclear Science Symposuim & Medical Imaging Conference Record, 2016)

Objektive und automatische Ermittlung der Verständlichkeit von Kindern und Jugendlichen mit Lippen-Kiefer-Gaumenspalten - Schuster, Maria; Maier, Andreas; Vogt, Beate; Nöth, Elmar; Nkenke, Emeka; Holst, Alexandra; Eysholdt, Ulrich; Rosanowski, Frank (Aktuelle phoniatrisch-pädaudiologische Aspekte 2006, 2006)

OCPAD - Occluded Checkerboard Pattern Detector - Fürsattel, Peter; Deitsch, Sergiu; Placht, Simon; Balda, Michael; Maier, Andreas; Riess, Christian (IEEE Winter Conference on Applications of Computer Vision, 2016)
Many camera calibration techniques require the detection of a pattern with known geometry, e.g., a checkerboard. Typically, the pattern must be fully contained in the field of view. This brings several limitations, one of which is that lens distortion can not reliably be estimated in outer image regions. This paper presents the occluded checkerboard pattern detector (OCPAD) to find checkerboards, even in a) low-resolution images, b) images with high lens distortion and if c) the pattern is partly occluded or not completely within the field of view. We exploit that checkerboards can easily be represented by a graph. We use graph matching to find the largest partial checkerboard in the image. Our detector complements a state-of-the-art calibration algorithm. Quantitatively, detection rates are considerably improved over the state-of-the-art. Additionally, estimation of lens distortion is greatly improved at outer image regions. Here, the reprojection error is improved by up to 50%.<br

OCR-D Compact: Results and State of Research in the Funding Initiative - Baierer, Konstantin; Boenig, Matthias; Engl, Elisabeth; Hartmann, Volker; Neudecker, Clemens; Altenhoener, Reinhard; Geyken, Alexander; Mangei, Johannes; Stotzka, Rainer; Dengel, Andreas; Jenckel, Martin; Gehrke, Alexander; et al. (Bibliothek Forschung und Praxis, 2020)
For some years, great efforts have been made to record and digitise the printed works of the 16th-18th centuries published in the German-speaking countries. Preparing their full-text transformation conceptually and technically is the overall goal of the DFG-project OCR-D, which is concerned with the further development of optical character recognition methods. The article describes the current state of development of the OCR-D software and analyses its initial testing in selected libraries.
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