Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides - Marzahl, Christian; Aubreville, Marc; Bertram, Christof A.; Stayt, Jason; Jasensky, Anne Katherine; Bartenschlager, Florian; Fragoso-Garcia, Marco; Barton, Ann K.; Elsemann, Svenja; Jabari, Samir; Krauth, Jens; Madhu, Prathmesh; et al. (Scientific Reports, 2020)
Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated inter- and intra-observer variability. Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages. Our deep learning-based approach reached a concordance of 0.85, partially exceeding human expert concordance (0.68 to 0.86, mean of 0.73, SD of 0.04). Intra-observer variability was high (0.68 to 0.88) and inter-observer concordance was moderate (Fleiss’ kappa = 0.67). Our object detection approach has a mean average precision of 0.66 over the five classes from the whole slide gigapixel image and a computation time of below two minutes. To mitigate the high inter- and intra-rater variability, we propose our automated object detection pipeline, enabling accurate, reproducible and quick EIPH scoring in WSI.
CRIS-Team Scopus Importer:2020-08-14

Deep Learning-Based Respiratory Motion Correction in Free-Breathing Abodiminal Diffusion-Weighted Imaging - Kim, Jinho; Gadjimuradov, Fasil; Benkert, Thomas; Vahle, Thomas; Maier, Andreas (Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, 2022)

Deep Learning based T2 distribution estimation for bone marrow characterization in inflammation and aging - Brock, Luise; Ben-Atya, Hadas; Saar, Galit; Folle, Lukas; Maier, Andreas; Freiman, Moti; Vandoorne, Katrien (European Molecular Imaging Meeting (EMIM), 2023)

Deep Learning basierte Vorhersage von Gliomwachstum mittels Growing Neural Cellular Automata - Huang, Yixing; Maier, Andreas; Sommer, Phillip; Fischer, Stefan; Srinivasan, Aishwarya Lakshmi; Fietkau, Rainer; Bert, Christoph; Putz, Florian (Strahlentherapie und Onkologie, 2022)
Keywords: deep learning; cellular automata; tumor growth

Deep Learning Compatible Differentiable X-ray Projections for Inverse Rendering - Shetty, Karthik; Birkhold, Annette; Strobel, Norbert; Jaganathan, Srikrishna; Kowarschik, Markus; Maier, Andreas; Egger, Bernhard (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Deep Learning Computed Tomography - Würfl, Tobias; Ghesu, Florin-Cristian; Christlein, Vincent; Maier, Andreas (Medical Image Computing and Computer-Assisted Intervention MICCAI 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.deeple

Deep Learning Computed Tomography: Learning Projection-Domain Weights from Image Domain in Limited Angle Problems - Würfl, Tobias; Hoffmann, Mathis; Christlein, Vincent; Breininger, Katharina; Huang, Yixing; Unberath, Mathias; Maier, Andreas (IEEE Transactions on Medical Imaging, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.deeple
Keywords: Image reconstruction; Neural networks; Machine learning; Geometry; Reconstruction algorithms; Iterative methods; Computed tomography

Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy - Weissmann, Thomas; Huang, Yixing; Fischer, Stefan; Roesch, Johannes; Mansoorian, Sina; Gaona, Horacio Ayala; Gostian, Antoniu-Oreste; Hecht, Markus; Lettmaier, Sebastian; Deloch, Lisa; Frey, Benjamin; Gaipl, Udo; et al. (Frontiers in Oncology, 2023)
Background: Deep learning-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. In particular, there is no publicly available open-source solution for large-scale autosegmentation of HN_LNL in the research setting. Methods: An expert-delineated cohort of 35 planning CTs was used for training of an nnU-net 3D-fullres/2D-ensemble model for autosegmentation of 20 different HN_LNL. A second cohort acquired at the same institution later in time served as the test set (n = 20). In a completely blinded evaluation, 3 clinical experts rated the quality of deep learning autosegmentations in a head-to-head comparison with expert-created contours. For a subgroup of 10 cases, intraobserver variability was compared to the average deep learning autosegmentation accuracy on the original and recontoured set of expert segmentations. A postprocessing step to adjust craniocaudal boundaries of level autosegmentations to the CT slice plane was introduced and the effect of autocontour consistency with CT slice plane orientation on geometric accuracy and expert rating was investigated. Results: Blinded expert ratings for deep learning segmentations and expert-created contours were not significantly different. Deep learning segmentations with slice plane adjustment were rated numerically higher (mean, 81.0 vs. 79.6, p = 0.185) and deep learning segmentations without slice plane adjustment were rated numerically lower (77.2 vs. 79.6, p = 0.167) than manually drawn contours. In a head-to-head comparison, deep learning segmentations with CT slice plane adjustment were rated significantly better than deep learning contours without slice plane adjustment (81.0 vs. 77.2, p = 0.004). Geometric accuracy of deep learning segmentations was not different from intraobserver variability (mean Dice per level, 0.76 vs. 0.77, p = 0.307). Clinical significance of contour consistency with CT slice plane orientation was not represented by geometric accuracy metrics (volumetric Dice, 0.78 vs. 0.78, p = 0.703). Conclusions: We show that a nnU-net 3D-fullres/2D-ensemble model can be used for highly accurate autodelineation of HN_LNL using only a limited training dataset that is ideally suited for large-scale standardized autodelineation of HN_LNL in the research setting. Geometric accuracy metrics are only an imperfect surrogate for blinded expert rating. <
Keywords: Deep learning; lymph node level; segmentation

Deep learning for brain metastasis detection and segmentation in longitudinal MRI data - Huang, Yixing; Bert, Christoph; Sommer, Philipp; Frey, Benjamin; Gaipl, Udo; Distel, Luitpold; Weissmann, Thomas; Uder, Michael; Schmidt, Manuel; Dörfler, Arnd; Maier, Andreas; Fietkau, Rainer; et al. (Medical Physics, 2022)
Keywords: Deep learning; brain metastasis; MRI; loss function; sensitivity specificity

Deep Learning for Magnetic Resonance Fingerprinting: Accelerating the Reconstruction of Quantitative Relaxation Maps - Hoppe, Elisabeth; Körzdorfer, Gregor; Nitka, Mathias; Würfl, Tobias; Wetzl, Jens; Lugauer, Felix; Schneider, Manuel; Pfeuffer, Josef; Maier, Andreas (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (26th Annual Meeting & Exhibition), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.deeple_0
Keywords: Quantitative Magnetic Resonance Imaging; Neural Networks; Convolutional Neural Networks

Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series - Hoppe, Elisabeth; Körzdorfer, Gregor; Würfl, Tobias; Wetzl, Jens; Lugauer, Felix; Pfeuffer, Josef; Maier, Andreas (German Medical Data Sciences: Visions and Bridges, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.deeple_2
Keywords: Deep Learning; Magnetic Resonance Imaging

Deep Learning for Orca Call Type Identification – A Fully Unsupervised Approach - Bergler, Christian; Schmitt, Manuel; Cheng, Rachael Xi; Maier, Andreas; Barth, Volker; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019)
Call type classification is an important instrument in bioacoustic investigating group-specific vocal repertoire, behavioral patterns, and cultures of different animal groups. There is a need using robust machine-based techniques to replace classification due to its advantages in handling large delivering consistent results, removing perceptual-based classification, and minimizing human errors. The current work is the first adopting a two-stage fully unsupervised approach on previous machine-segmented orca data to identify orca sound types using deep learning together with one of the largest bioacoustic datasets – the Orchive. The proposed methods include: (1) unsupervised feature learning using an undercomplete ResNet18-autoencoder trained on machine-annotated data, and (2) spectral clustering utilizing compressed orca feature representations. An existing human-labeled orca dataset was clustered, including 514 signals distributed over 12 classes. This two-stage fully unsupervised approach is an initial study to (1) examine machine-generated clusters against human-identified orca call type classes, (2) compare supervised call type classification versus unsupervised call type clustering, and (3) verify the general feasibility of a completely unsupervised approach based on machine-labeled orca data resulting in a major progress within the research field of animal linguistics, by deriving a much deeper understanding and facilitating totally new insights and opportunities.
CRIS-Team Scopus Importer:2019-11-19
Keywords: orca; call type; unsupervised; deep learning; clustering

Deep learning for perspective deformation correction in X-ray imaging - Huang, Yixing; Maier, Andreas; Fan, Fuxin; Kreher, Björn W.; Huang, Xiaolin; Fietkau, Rainer; Bert, Christoph; Putz, Florian (Radiotherapy and Oncology, 2023)
Keywords: Deep learning; perspective deformation; X-ray imaging

Deep learning for terahertz image denoising in nondestructive historical document analysis - Dutta, Balaka; Root, Konstantin; Ullmann, Ingrid; Wagner, Fabian; Mayr, Martin; Seuret, Mathias; Thies, Mareike; Stromer, Daniel; Christlein, Vincent; Schür, Jan; Maier, Andreas; Huang, Yixing (Scientific Reports, 2022)
Historical documents contain essential information about the past, including places, people, or events. Many of these valuable cultural artifacts cannot be further examined due to aging or external influences, as they are too fragile to be opened or turned over, so their rich contents remain hidden. Terahertz (THz) imaging is a nondestructive 3D imaging technique that can be used to reveal the hidden contents without damaging the documents. As noise or imaging artifacts are predominantly present in reconstructed images processed by standard THz reconstruction algorithms, this work intends to improve THz image quality with deep learning. To overcome the data scarcity problem in training a supervised deep learning model, an unsupervised deep learning network (CycleGAN) is first applied to generate paired noisy THz images from clean images (clean images are generated by a handwriting generator). With such synthetic noisy-to-clean paired images, a supervised deep learning model using Pix2pixGAN is trained, which is effective to enhance real noisy THz images. After Pix2pixGAN denoising, 99% characters written on one-side of the Xuan paper can be clearly recognized, while 61% characters written on one-side of the standard paper are sufficiently recognized. The average perceptual indices of Pix2pixGAN processed images are 16,83, which is very close to the average perceptual index 16.19 of clean handwriting images. Our work has important value for THz-imaging-based nondestructive historical document analysis.<br
Keywords: Deep learning; Terahertz imaging; Historical document analysis; Nondestructive testing

Deep learning fundamentals - Breininger, Katharina; Christlein, Vincent; Würfl, Tobias; Maier, Andreas (Informatik aktuell, 2018)
Deep learning has received a lot of attention in the machine learning community. Successful applications from speech recognition or computer vision are already part of our daily life. Much effort has been devoted to transferring this success to medical image computing.
CRIS-Team Scopus Importer:2020-11-20

Deep Learning Glaucoma Classification in enface OCTA scans of the human macular region - Schottenhamml, Julia; Ploner, Stefan; Husvogt, Lennart; Mardin, Sophia; Hohberger, Bettina; Mardin, Christian Y.; Maier, Andreas (ARVO Annual Meeting 2021, 2021)
CRIS-Team WoS Importer:2021-09-24

Deep Learning-Guided Weighted Averaging for Signal Dropout Compensation in DWI of the Liver - Gadjimuradov, Fasil; Benkert, Thomas; Nickel, Marcel Dominik; Führes, Tobit; Saake, Marc; Maier, Andreas (Magnetic Resonance in Medicine, 2022)
Purpose To develop an algorithm for the retrospective correction of signal dropout artifacts in abdominal DWI resulting from cardiac motion. Methods Given a set of image repetitions for a slice, a locally adaptive weighted averaging is proposed that aims to suppress the contribution of image regions affected by signal dropouts. Corresponding weight maps were estimated by a sliding-window algorithm, which analyzed signal deviations from a patch-wise reference. In order to ensure the computation of a robust reference, repetitions were filtered by a classifier that was trained to detect images corrupted by signal dropouts. The proposed method, named Deep Learning-guided Adaptive Weighted Averaging (DLAWA), was evaluated in terms of dropout suppression capability, bias reduction in the ADC, and noise characteristics. Results In the case of uniform averaging, motion-related dropouts caused signal attenuation and ADC overestimation in parts of the liver, with the left lobe being affected particularly. Both effects could be substantially mitigated by DLAWA while preventing global penalties with respect to SNR due to local signal suppression. Performing evaluations on patient data, the capability to recover lesions concealed by signal dropouts was demonstrated as well. Further, DLAWA allowed for transparent control of the trade-off between SNR and signal dropout suppression by means of a few hyperparameters. Conclusion This work presents an effective and flexible method for the local compensation of signal dropouts resulting from motion and pulsation. Because DLAWA follows a retrospective approach, no changes to the acquisition are required.
CRIS-Team WoS Importer:2022-08-12

Deep Learning in Surgical Workflow Analysis: A Review of Phase and Step Recognition - Demir, Kubilay; Schieber, Hannah; Weise, Tobias; May, Matthias; Maier, Andreas; Yang, Seung Hee (IEEE Journal of Biomedical and Health Informatics, 2023)
Objective: In the last two decades, there has been a growing interest in exploring surgical procedures with statistical models to analyze operations at different semantic levels. This information is necessary for developing context-aware intelligent systems, which can assist the physicians during operations, eval-uate procedures afterward or help the management team to effec-tively utilize the operating room. The objective is to extract reliable patterns from surgical data for the robust estimation of surgical activities performed during operations. The purpose of this paper is to review the state-of-the-art deep learning methods that have been published after 2018 for analyzing surgical workflows, with a focus on phase and step recognition. Methods: Three databases, IEEE Xplore, Scopus, and PubMed were searched, and additional studies are added through a manual search. After the database search, 343 studies were screened and a total of 45 studies are selected for this review. Conclusion: The use of temporal information is essential for identifying the next surgical action. Contemporary methods used mainly RNNs, hierarchical CNNs, and Transformers to preserve long-distance temporal relations. The lack of large publicly available datasets for various procedures is a great challenge for the development of new and robust models. As supervised learning strategies are used to show proof-of-concept, self-supervised, semi-supervised, or active learning methods are used to mitigate dependency on annotated data. Significance: The present study provides a comprehensive review of recent methods in surgical workflow analysis, summarizes commonly used archi-tectures, datasets, and discusses challenges.
CRIS-Team Scopus Importer:2023-09-29
Keywords: Analytical models; Cataracts; Databases; Deep learning; deep learning; Laparoscopes; Recording; Surgery; surgical data science; surgical phase recognition; Surgical workflow analysis

Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density - Folle, Lukas; Meinderink, Timo; Simon, David; Liphardt, Anna-Maria; Krönke, Gerhard; Schett, Georg; Kleyer, Arnd; Maier, Andreas (Scientific Reports, 2021)
Arthritis patients develop hand bone loss, which leads to destruction and functional impairment of the affected joints. High resolution peripheral quantitative computed tomography (HR-pQCT) allows the quantification of volumetric bone mineral density (vBMD) and bone microstructure in vivo with an isotropic voxel size of 82 micrometres. However, image-processing to obtain bone characteristics is a time-consuming process as it requires semi-automatic segmentation of the bone. In this work, a fully automatic vBMD measurement pipeline for the metacarpal (MC) bone using deep learning methods is introduced. Based on a dataset of HR-pQCT volumes with MC measurements for 541 patients with arthritis, a segmentation network is trained. The best network achieves an intersection over union as high as 0.94 and a Dice similarity coefficient of 0.97 while taking only 33 s to process a whole patient yielding a speedup between 2.5 and 4.0 for the whole workflow. Strong correlation between the vBMD measurements of the expert and of the automatic pipeline are achieved for the average bone density with 0.999 (Pearson) and 0.996 (Spearman’s rank) with p < 0.001for all correlations. A qualitative assessment of the network predictions and the manual annotations yields a 65.9% probability that the expert favors the network predictions. Further, the steps to integrate the pipeline into the clinical workflow are shown. In order to make these workflow improvements available to others, we openly share the code of this work.
Keywords: Rheumatic diseases; segmentation; deep learning

Deep Multi-Frame Filtering for Hearing Aids - Schröter, Hendrik; Rosenkranz, Tobias; Escalante-B, Alberto N.; Maier, Andreas (INTERSPEECH 2023, 2023)
Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal. Deep filtering (DF) recently demonstrated its capabilities for low-latency scenarios like hearing aids with its complex multi-frame (MF) filter. Alternatively, the complex filter can be estimated via an MF minimum variance distortionless response (MVDR), or MF Wiener filter (WF). Previous studies have shown that incorporating algorithm domain knowledge using an MVDR filter might be beneficial compared to the direct filter estimation via DF. In this work, we compare the usage of various multi-frame filters such as DF, MF-MVDR, or MF-WF for HAs. We assess different covariance estimation methods for both MF-MVDR and MF-WF and objectively demonstrate an improved performance compared to direct DF estimation, significantly outperforming related work while improving the runtime performance.
Keywords: hearing aids; speech enhancement; multi-frame filtering

Deepnapsi: Deep Learning for Nail Psoriasis Prediction - Folle, Lukas; Fenzl, Pauline; Fagni, Filippo; Thies, Mareike; Christlein, Vincent; Meder, Christine; Sticherling, Michael; Simon, David; Schett, Georg; Maier, Andreas; Kleyer, Arnd (Proceedings - International Symposium on Biomedical Imaging, 2023)
Nail psoriasis is a frequent condition that is associated with a severe course of rheumatic diseases. Thus, it may be used to adapt the therapy of patients according to the change of nail psoriasis. The nail psoriasis severity index (NAPSI) was developed to measure this condition. Nevertheless, there is a lack of application for the NAPSI, as its use in the clinic is too time-consuming. In this work, we propose the use of advancements in deep learning in a new domain by recording, annotating, and predicting the NAPSI based on photographs of the hand and term our approach DeepNAPSI. This allows not just the automated recording of the NAPSI in the clinic, but also patient self-assessment from home. Our method achieved an area-under-receiver-operator-characteristic curve (AUROC) of 0.83 and 0.86 for macro and micro averaging, respectively, and a mean absolute error of 0.55.
CRIS-Team Scopus Importer:2023-10-06
Keywords: Deep Learning; Mobile device; Nail psoriasis; Precision medicine

DeepNAPSI multi-reader nail psoriasis prediction using deep learning - Folle, Lukas; Fenzl, Pauline; Fagni, Filippo; Thies, Mareike; Christlein, Vincent; Meder, Christine; Simon, David; Minnopoulou, Ioanna; Sticherling, Michael; Schett, Georg; Maier, Andreas; Kleyer, Arnd (Nature Scientific Reports, 2023)
Nail psoriasis occurs in about every second psoriasis patient. Both, finger and toe nails can be affected and also severely destroyed. Furthermore, nail psoriasis is associated with a more severe course of the disease and the development of psoriatic arthritis. User independent quantification of nail psoriasis, however, is challenging due to the heterogeneous involvement of matrix and nail bed. For this purpose, the nail psoriasis severity index (NAPSI) has been developed. Experts grade pathological changes of each nail of the patient leading to a maximum score of 80 for all nails of the hands. Application in clinical practice, however, is not feasible due to the time-intensive manual grading process especially if more nails are involved. In this work we aimed to automatically quantify the modified NAPSI (mNAPSI) of patients using neuronal networks retrospectively. First, we performed photographs of the hands of patients with psoriasis, psoriatic arthritis, and rheumatoid arthritis. In a second step, we collected and annotated the mNAPSI scores of 1154 nail photos. Followingly, we extracted each nail automatically using an automatic key-point-detection system. The agreement among the three readers with a Cronbach’s alpha of 94% was very high. With the nail images individually available, we trained a transformer-based neural network (BEiT) to predict the mNAPSI score. The network reached a good performance with an area-under-receiver-operator-curve of 88% and an area-under precision-recall-curve (PR-AUC) of 63%. We could compare the results with the human annotations and achieved a very high positive Pearson correlation of 90% by aggregating the predictions of the network on the test set to the patient-level. Lastly, we provided open access to the whole system enabling the use of the mNAPSI in clinical practice.

Deep Neural Networks for Motion Estimation in k-space: Applications and Design - Hoßbach, Julian; Splitthoff, Daniel; Haskell, Melissa; Cauley, Stephen; Meyer, Heiko; Pfeuffer, Josef; Maier, Andreas (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB, 2019)

Deep OCT angiography image generation for motion artifact suppression - Hoßbach, Julian; Husvogt, Lennart; Kraus, Martin; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2020)
Eye movements, blinking and other motion during the acquisition of optical coherence tomography (OCT) can lead to artifacts, when processed to OCT angiography (OCTA) images. Affected scans emerge as high intensity (white) or missing (black) regions, resulting in lost information. The aim of this research is to fill these gaps using a deep generative model for OCT to OCTA image translation relying on a single intact OCT scan. Therefore, a U-Net is trained to extract the angiographic information from OCT patches. At inference, a detection algorithm finds outlier OCTA scans based on their surroundings, which are then replaced by the trained network. We show that generative models can augment the missing scans. The augmented volumes could then be used for 3-D segmentation or increase the diagnostic value.
CRIS-Team Scopus Importer:2020-04-21

Deep Recurrent Partial Fourier Reconstruction in Diffusion MRI - Gadjimuradov, Fasil; Benkert, Thomas; Nickel, Marcel Dominik; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020)
Partial Fourier (PF) acquisition schemes are often employed to increase the inherently low SNR in diffusion-weighted (DW) images. The resulting ill-posed reconstruction problem can be tackled by an iterative Projection Onto Convex Sets (POCS). By relaxing the data constraint and replacing the heuristically chosen regularization by learned convolutional filters, we arrive at an unrolled recurrent network architecture which circumvents weaknesses of the conventional POCS. Further, knowledge on the pixel-wise noise level of MR images is incorporated into data consistency operations within the reconstruction network. We are able to demonstrate on DW images of the pelvis that the proposed model quantitatively and qualitatively outperforms conventional methods as well as a U-Net representing a direct image-to-image mapping.
CRIS-Team Scopus Importer:2020-12-04
Keywords: Deep learning; Diffusion MRI; Partial Fourier

Deep Representation Learning for Orca Call Type Classification - Bergler, Christian; Schmitt, Manuel; Cheng, Rachael Xi; Schröter, Hendrik; Maier, Andreas; Barth, Volker; Weber, Michael; Nöth, Elmar (Text, Speech, and Dialogue, 22nd International Conference, TSD 2019, Ljubljana, Slovenia, September 11–13, 2019, Proceedings, 2019)
Marine mammals produce a wide variety of vocalizations. There is a growing need for robust automatic classification methods especially in noisy underwater environments in order to access large amounts of bioacoustic signals and to replace tedious and error prone human perceptual classification. In case of the northern resident killer whale (Orcinus orca), echolocation clicks, whistles, and pulsed calls make up its vocal repertoire. Pulsed calls are the most intensively studied type of vocalization. In this study we propose a hybrid call type classification approach outperforming our previous work on supervised call type classification consisting of two components: (1) deep representation learning of killer whale sounds by investigating various autoencoder architectures and data corpora and (2) subsequent supervised training of a ResNet18 call type classifier on a much smaller dataset by using the pre-trained representations. The best semi-supervised trained classification model achieved a test accuracy of 96% and a mean test accuracy of 94% outperforming our previous work by 7% points.
CRIS-Team Scopus Importer:2019-10-15
Keywords: Bioacoustics; Call type; Classification; Deep learning; Killer whale; Orca; Representation learning

Deep Scatter Splines: Learning-Based Medical X-ray Scatter Estimation Using B-splines - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Syben-Leisner, Christopher; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (The 6th International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2020)

Defining a baseline identification of artifacts in confocal laser endomicroscopy in head and neck cancer imaging - Pan, Zhaoya; Breininger, Katharina; Aubreville, Marc; Stelzle, Florian; Maier, Andreas; Oetter, Nicolai; Mantsopoulos, Konstantinos; Iro, Heinrich; Goncalves, Miguel; Sievert, Matti (American Journal of Otolaryngology, 2023)
Purpose: Probe-based confocal laser endomicroscopy (pCLE) allows imaging of the laryngeal mucosa in a thousand-fold magnification. However, the existence of multiple nondiagnostic frames might hinder the performance of automated systems, especially in post-processing and analysis algorithms. This study aims to investigate the feasibility of building a baseline for the identification of diagnostic frames and to evaluate the influence of sequence information in this task. Materials and methods: We included eight patients with squamous cell carcinoma (SCC) and planned total laryngectomy between October 2020 and February 2021. One hundred frames were randomly selected out of 21,272 pCLE frames of the tumor and healthy mucosa during surgery. Three experts were asked to classify these frames into diagnostic and nondiagnostic ones with different subtypes under two settings, i.e., isolated frames or short video sequences. Inter-rater and intra-rater consistency were analyzed with Cohen's kappa coefficients. Results: Among all raters, regarding the classification of diagnostic frames, an average agreement of κ = 0.79 has been achieved. The overall consistency for the subtypes of nondiagnostic frames is κ = 0.75 and κ = 0.57, respectively. Conclusions: The high level of consistency regarding the identification of diagnostic frames shows the reliability of the classification results and builds the foundation for future automatic algorithms.
CRIS-Team Scopus Importer:2023-01-06
Keywords: Classification system; Confocal laser endomicroscopy; Head and neck cancer; Larynx; Non-invasive histological imaging; Pharynx

Deformable Respiratory Motion Correction for Hepatic Rotational Angiography - Klugmann, Alexandra; Bier, Bastian; Müller, Kerstin; Maier, Andreas; Unberath, Mathias (Computerized Medical Imaging and Graphics, 2018)
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.deform
Keywords: Discrete optimization; Image reconstruction; Interventional imaging; Motion modeling

Degenerating u-net on retinal vessel segmentation: What do we really need? - Fu, Weilin; Breininger, Katharina; Pan, Zhaoya; Maier, Andreas (Informatik aktuell, 2020)
Retinal vessel segmentation is an essential step for fundus image analysis. With the recent advances of deep learning technologies, many convolutional neural networks have been applied in this field, including the successful U-Net. In this work, we firstly modify the U-Net with functional blocks aiming to pursue higher performance. The absence of the expected performance boost then lead us to dig into the opposite direction of shrinking the U-Net and exploring the extreme conditions such that its segmentation performance is maintained. Experiment series to simplify the network structure, reduce the network size and restrict the training conditions are designed. Results show that for retinal vessel segmentation on DRIVE database, U-Net does not degenerate until surprisingly acute conditions: one level, one filter in convolutional layers, and one training sample. This experimental discovery is both counter-intuitive and worthwhile. Not only are the extremes of the U-Net explored on a well-studied application, but also one intriguing warning is raised for the research methodology which seeks for marginal performance enhancement regardless of the resource cost.
CRIS-Team Scopus Importer:2020-04-21

Denoising and artefact reduction in dynamic flat detector CT perfusion imaging using high speed acquisition: first experimental and clinical results - Manhart, Michael; Aichert, André; Struffert, Tobias; Deuerling-Zheng, Yu; Kowarschik, Markus; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Physics in Medicine and Biology, 2014)
Flat detector CT perfusion (FD-CTP) is a novel technique using C-arm angiography systems for interventional dynamic tissue perfusion measurement with high potential benefits for catheter-guided treatment of stroke. However, FD-CTP is challenging since C-arms rotate slower than conventional CT systems. Furthermore, noise and artefacts affect the measurement of contrast agent flow in tissue. Recent robotic C-arms are able to use high speed protocols (HSP), which allow sampling of the contrast agent flow with improved temporal resolution. However, low angular sampling of projection images leads to streak artefacts, which are translated to the perfusion maps. We recently introduced the FDK-JBF denoising technique based on Feldkamp (FDK) reconstruction followed by joint bilateral filtering (JBF). As this edge-preserving noise reduction preserves streak artefacts, an empirical streak reduction (SR) technique is presented in this work. The SR method exploits spatial and temporal information in the form of total variation and time-curve analysis to detect and remove streaks. The novel approach is evaluated in a numerical brain phantom and a patient study. An improved noise and artefact reduction compared to existing post-processing methods and faster computation speed compared to an algebraic reconstruction method are achieved. © 2014 Institute of Physics and Engineering in Medicine.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.denois_8
Keywords: 3D reconstruction; flat detector CT; noise reduction; perfusion imaging; stroke treatment

Deriving Neural Network Architectures Using Precision Learning: Parallel-to-Fan Beam Conversion - Syben-Leisner, Christopher; Stimpel, Bernhard; Lommen, Jonathan; Würfl, Tobias; Dörfler, Arnd; Maier, Andreas (40th German Conference on Pattern Recognition, GCPR 2018, 2019)
Keywords: Hybrid MRI/X-ray imaging; Precision learning; Machine learning

Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees - Bier, Bastian; Aschoff, Katharina; Syben-Leisner, Christopher; Unberath, Mathias; Levenston, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (MACHINE LEARNING FOR MEDICAL IMAGE RECONSTRUCTION, MLMIR 2018, 2018)
CRIS-Team WoS Importer:2019-08-09

Detecting and Measuring Surface Area of Skin Lesions - Mirzaalian Dastjerdi, Houman; Töpfer, Dominique; Bangemann, Michael; Maier, Andreas (Bildverarbeitung für die Medizin 2018, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.detect

Detection of Large Vessel Occlusions using Deep Learning by Deforming Vessel Tree Segmentations - Thamm, Florian; Taubmann, Oliver; Jürgens, Markus; Ditt, Hendrik; Maier, Andreas (Bildverarbeitung für die Medizin 2022, 2022)
Computed Tomography Angiography is a key modality providing insights into the cerebrovascular vessel tree that are crucial for the diagnosis and treatment of ischemic strokes, in particular in cases of large vessel occlusions (LVO). Thus, the clinical workflow greatly benefits from an automated detection of patients suffering from LVOs. This work uses convolutional neural networks for case-level classification trained with elastic deformation of the vessel tree segmentation masks to artificially augment training data. Using only masks as the input to our model uniquely allows us to apply such deformations much more aggressively than one could with conventional image volumes while retaining sample realism. The neural network classifies the presence of an LVO and the affected hemisphere. In a 5-fold cross validated ablation study, we demonstrate that the use of the suggested augmentation enables us to train robust models even from few data sets. Training the EfficientNetB1 architecture on 100 data sets, the proposed augmentation scheme was able to raise the ROC AUC to 0.85 from a baseline value of 0.57 using no augmentation. The best performance was achieved using a 3D-DenseNet yielding an AUC of 0.88. The augmentation had positive impact in classification of the affected hemisphere as well, where the 3D-DenseNet reached an AUC of 0.93 on both sides.<br
Keywords: Computed Tomography Angiography; Large Vessel Occlusions; Deep Learning

Detection of Pulmonary Embolisms in NCCT Data Using nnDetection - Vorberg, Linda; Thamm, Florian; Ditt, Hendrik; Horger, Marius; Hagen, Florian; Maier, Andreas (Informatik aktuell, 2023)
Pulmonary embolism (PE) is a life-threatening disease for which a prompt diagnosis is important and challenging as its symptoms are mostly nonspecific. In the clinical workflow of diagnosing PE, computed tomographic pulmonary angiography (CTPA) has become the gold standard imaging tool. As the performance of a CT scan with contrast agent sometimes can be contraindicated and is associated with high costs, identifying the embolism with a non-contrast CT (NCCT) scan is desirable. We automated the detection of PE in NCCT scans with the use of deep learning, in order to guide the physician and speed up the clinical workflow. We used nnDetection, designed for medical object detection, to accomplish this task. nnDetection gets informed by additional channels which are used besides the NCCT scan. These are segmentation masks of the lung lobes and vessels which are also used for post-processing. In a study with 99 patients that all presented with PE, nnDetection was shown to detect a PE in 71% of the cases when considering the first 10 boxes with the highest probability containing a PE.
Created from Fastlane, Scopus look-up

Detection of Unseen Low-Contrast Signals Using Classic and Novel Model Observers - Xu, Yang; Schebesch, Frank; Ravikumar, Nishant; Maier, Andreas (Informatik aktuell, 2019)
Automatic task-based image quality assessment has been of importance in various clinical and research applications. In this paper, we propose a neural network model observer, a novel concept which has recently been investigated. It is trained and tested on simulated images with different contrast levels, with the aim of trying to distinguish images based on their quality/contrast. Our model shows promising properties that its output is sensitive to image contrast, and generalizes well to unseen low-contrast signals. We also compare the results of the proposed approach with those of a channelized hotelling observer (CHO), on the same simulated dataset.
CRIS-Team Scopus Importer:2019-05-14

Determination of Forming Limits in Sheet Metal Forming Using Deep Learning - Jaremenko, Christian; Ravikumar, Nishant; Affronti, Emanuela; Merklein, Marion; Maier, Andreas (Materials, 2019)
The forming limit curve (FLC) is used to model the onset of sheet metal instability during forming processes e.g., in the area of finite element analysis, and is usually determined by evaluation of strain distributions, derived with optical measurement systems during Nakajima tests. Current methods comprise of the standardized DIN EN ISO 12004-2 or time-dependent approaches that heuristically limit the evaluation area to a fraction of the available information and show weaknesses in the context of brittle materials without a pronounced necking phase. To address these limitations, supervised and unsupervised pattern recognition methods were introduced recently. However, these approaches are still dependent on prior knowledge, time, and localization information. This study overcomes these limitations by adopting a Siamese convolutional neural network (CNN), as a feature extractor. Suitable features are automatically learned using the extreme cases of the homogeneous and inhomogeneous forming phase in a supervised setup. Using robust Student's t mixture models, the learned features are clustered into three distributions in an unsupervised manner that cover the complete forming process. Due to the location and time independency of the method, the knowledge learned from formed specimen up until fracture can be transferred on to other forming processes that were prematurely stopped and assessed using metallographic examinations, enabling probabilistic cluster membership assignments for each frame of the forming sequence. The generalization of the method to unseen materials is evaluated in multiple experiments, and additionally tested on an aluminum alloy AA5182, which is characterized by Portevin-LE Chatlier effects.
CRIS-Team WoS Importer:2019-05-07

Development and validation of a classification and scoring system for the diagnosis of oral squamous cell carcinomas through confocal laser endomicroscopy - Oetter, Nicolai; Knipfer, Christian; Rohde, Maximilian; von Wilmowsky, Cornelius; Maier, Andreas; Brunner, Kathrin; Adler, Werner; Neukam, Friedrich Wilhelm; Neumann, Helmut; Stelzle, Florian (Journal of Translational Medicine, 2016)
Confocal laser endomicroscopy (CLE) is an optical biopsy method allowing in vivo microscopic imaging at 1000-fold magnification. It was the aim to evaluate CLE in the human oral cavity for the differentiation of physiological/carcinomatous mucosa and to establish and validate, for the first time, a scoring system to facilitate CLE assessment.The study consisted of 4 phases: (1) CLE-imaging (in vivo) was performed after the intravenous injection of fluorescein in patients with histologically confirmed carcinomatous oral mucosa; (2) CLE-experts (n = 3) verified the applicability of CLE in the oral cavity for the differentiation between physiological and cancerous tissue compared to the gold standard of histopathological assessment; (3) based on specific patterns of tissue changes, CLE-experts (n = 3) developed a classification and scoring system (DOC-Score) to simplify the diagnosis of oral squamous cell carcinomas; (4) validation of the newly developed DOC-Score by non-CLE-experts (n = 3); final statistical evaluation of their classification performance (comparison to the results of CLE-experts and the histopathological analyses).Experts acquired and edited 45 sequences (260 s) of physiological and 50 sequences (518 s) of carcinomatous mucosa (total: 95 sequences/778 s). All sequences were evaluated independently by experts and non-experts (based on the newly proposed classification system). Sensitivity (0.953) and specificity (0.889) of the diagnoses by experts as well as sensitivity (0.973) and specificity (0.881) of the non-expert ratings correlated well with the results of the present gold standard of tissue histopathology. Experts had a positive predictive value (PPV) of 0.905 and a negative predictive value (NPV) of 0.945. Non-experts reached a PPV of 0.901 and a NPV of 0.967 with the help of the DOC-Score. Inter-rater reliability (Fleiss` kappa) was 0.73 for experts and 0.814 for non-experts. The intra-rater reliability (Cronbach's alpha) of the experts was 0.989 and 0.884 for non-experts.CLE is a suitable and valid method for experts to diagnose oral cancer. Using the DOC-Score system, an accurate chair-side diagnosis of oral cancer is feasible with comparable results to the gold standard of histopathology-even in daily clinical practice for non-experienced raters.
EVALuna2:2055

Diaphragm Tracking for Respiratory Motion Compensated Cardiac C-Arm CT - Bögel, Marco; Maier, Andreas; Hofmann, Hannes; Hornegger, Joachim; Fahrig, Rebecca (Proceedings of the second international conference on image formation in x-ray computed tomography, 2012)

Diaphragm Tracking in Cardiac C-Arm Projection Data - Bögel, Marco; Maier, Andreas; Hofmann, Hannes; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung für die Medizin 2011, 2012)

Diffeomorphic Image Registration for CINE Cardio MR images using deep learning - Amsel, Daniel; Yoon, Seung Su; Wetzl, Jens; Maier, Andreas (Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, 2022)

Differential diagnosis of RA and PsA using neural networks on 3D bone shape of finger joints - Folle, Lukas; Liu, Chang; Simon, David; Meinderink, Timo; Liphardt, Anna-Maria; Krönke, Gerhard; Schett, Georg; Maier, Andreas; Kleyer, Arnd (Annals of the Rheumatic Diseases 2021, 2021)
Keywords: Deep Learning; Rheumatoid arthritis; psoriatic arthritis

Differential Tomography: Influence of Sensitivity Direction and Noise-suppressing Windows - Käppler, Sebastian; Maier, Andreas; Riess, Christian (Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography (CT-Meeting), 2018)
Keywords: X-ray Phase Contrast; Tomography; Noise; Grating orientation

Digital health in smart cities: Rethinking the remote health monitoring architecture on combining edge, fog, and cloud - Rodrigues, Vinicius Facco; da Rosa Righi, Rodrigo; da Costa, Cristiano André; Zeiser, Felipe Andre; Eskofier, Björn; Maier, Andreas; Kim, Daeyoung (Health and Technology, 2023)
Purpose: Smart cities that support the execution of health services are more and more in evidence today. Here, it is mainstream to use IoT-based vital sign data to serve a multi-tier architecture. The state-of-the-art proposes the combination of edge, fog, and cloud computing to support critical health applications efficiently. However, to the best of our knowledge, initiatives typically present the architectures, not bringing adaptation and execution optimizations to address health demands fully. Methods: This article introduces the VitalSense model, which provides a hierarchical multi-tier remote health monitoring architecture in smart cities by combining edge, fog, and cloud computing. Results: Although using a traditional composition, our contributions appear in handling each infrastructure level. We explore adaptive data compression and homomorphic encryption at the edge, a multi-tier notification mechanism, low latency health traceability with data sharding, a Serverless execution engine to support multiple fog layers, and an offloading mechanism based on service and person computing priorities. Conclusions: This article details the rationale behind these topics, describing VitalSense use cases for disruptive healthcare services and preliminary insights regarding prototype evaluation.
CRIS-Team Scopus Importer:2023-05-05
Keywords: Digital health; Edge computing; Fog computing; Internet of things; Remote health monitoring; Smart city

Digital Twinning of Dynamic Anthropomorphic Population for Assessment of Data Driven Respiratory Gating Methods in SPECT - Reymann, Maximilian; Massanes, Francesc; Kuwert, Torsten; Vija, Hans; Maier, Andreas (IEEE Nuclear Science Symposium and Medical Imaging Conference, 2022)
Data Driven Gating (DDG) can be used to improve quantification in Myocardial Perfusion Imaging (MPI), given a reliable estimate of the respiratory motion. Current algorithms for extracting a respiratory surrogate have shown large variances on patient data, but it remains unclear what causes this reduced accuracy for most patients. We propose a method for creating large sets of digital twins that can be used to assess the performance of DDG algorithms, and give an example application of our method. We simulate a set of XCAT phantoms enabled with respiratory motion recorded from an Anzai belt and perform Monte Carlo Simulation (MCS) using SIMIND for each time frame. By performing high-count simulations of different regions of the phantoms separately, we are able to create different uptake ratios and count levels of the phantoms. Using this data set we assess the effect of a static-high uptake region in the projection data, represented by the gall bladder, and show the influence on the Laplacian Eigenmaps (LE) and Center of Light (COL) DDG method. The presented method enables the assessment of many factors on DDG and we are planning on performing further experiments.<br

Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI - Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas (STACOM 2018: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges, 2019)
Segmentation of the left atrial chamber and assessing its morphology, are essential for improving our understanding of atrial fibrillation, the most common type of cardiac arrhythmia. Automation of this process in 3D gadolinium enhanced-MRI (GE-MRI) data is desirable, as manual delineation is time-consuming, challenging and observer-dependent. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. However, it is difficult to incorporate local and global information without using contracting (pooling) layers, which in turn reduces segmentation accuracy for smaller structures. In this paper, we propose a 3D CNN for volumetric segmentation of the left atrial chamber in LGE-MRI. Our network is based on the well known U-Net architecture. We employ a 3D fully convolutional network, with dilated convolutions in the lowest level of the network, and residual connections between encoder blocks to incorporate local and global knowledge. The results show that including global context through the use of dilated convolutions, helps in domain adaptation, and the overall segmentation accuracy is improved in comparison to a 3D U-Net.
CRIS-Team Scopus Importer:2019-05-02

Dilated deeply supervised networks for hippocampus segmentation in MRI - Folle, Lukas; Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas (Informatik aktuell, 2019)
Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimers Disease (AD). The shape and structure of the hippocampus are important factors in terms of early AD diagnosis and prognosis by clinicians. However, manual segmentation of such subcortical structures in MR studies is a challenging and subjective task. In this paper, we investigate variants of the well known 3D U-Net, a type of convolution neural network (CNN) for semantic segmentation tasks.We propose an alternative form of the 3D U-Net, which uses dilated convolutions and deep supervision to incorporate multi-scale information into the model. The proposed method is evaluated on the task of hippocampus head and body segmentation in an MRI dataset, provided as part of the MICCAI 2018 segmentation decathlon challenge. The experimental results show that our approach outperforms other conventional methods in terms of different segmentation accuracy metrics.
CRIS-Team Scopus Importer:2019-05-14

Direct and indirect image rotation estimation methods of orthopedic x-ray images - Kunze, Holger; Kordon, Florian Johannes; Maier, Andreas; Breininger, Katharina (Medical Imaging 2022: Image Processing, 2022)
For many medical questions, X-ray imaging belongs to the gold standard for diagnosis, treatment planning, treatment guidance, and surgery assessment. To improve the reading performance, standardized image rotation is an important step. We propose a new algorithm to estimate the correct image rotation. For many body regions, one line can be defined that is aligned with the upright orientation of the X-ray image. This line can be, for example, the shaft axis of a long bone or the axis of the spine. In this paper, we propose a strategy to automatically align X-ray images with their standard orientation. In a first step, the heatmap of this line is determined using the segmentation network D-LinkNet. The rotation direction, up to a top-down flip, is obtained by computing the orientation of the main axis of this heatmap. For the orientation computation, we compare PCA and Hu moments. While the PCA requires to threshold the heatmap, Hu moments can be used directly on the output values of the network, preserving the (un)certainty of the segmentation. We compare these two methods with a ResNet-18 for the direct estimation of the image rotation on 220 X-ray images from the MURA dataset showing the wrist in the AP view. With the heatmap-based approach followed by Hu moments analysis, the median absolute error for the angle estimation can be reduced to 0.7° compared to 1.7° by a direct estimation method. PCA suffers from noisy heatmaps for images of bad quality degrading the overall performance of this approach.
Keywords: Medial Image Analysis; Rotation Estimation; X-ray Images; Orthopedics

Discrete Estimation of Data Completeness for 3D Scan Trajectories with Detector Offset - Maier, Andreas; Kugler, Patrick; Lauritsch, Günter; Hornegger, Joachim (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.discre_2
Keywords: Data Completeness; Computed Tomography; Tuy's Condition

Disentangled Latent Speech Representation for Automatic Pathological Intelligibility Assessment - Weise, Tobias; Maier, Andreas; Nöth, Elmar; Heismann, Bjoern; Schuster, Maria; Yang, Seung Hee (Proceedings of INTERSPEECH 2022, 2022)
Speech intelligibility assessment plays an important role in the therapy of patients suffering from pathological speech disorders. Automatic and objective measures are desirable to assist therapists in their traditionally subjective and labor-intensive assessments. In this work, we investigate a novel approach for obtaining such a measure using the divergence in disentangled latent speech representations of a parallel utterance pair, obtained from a healthy reference and a pathological speaker. Experiments on an English database of Cerebral Palsy patients, using all available utterances per speaker, show high and significant correlation values (R = −0.9) with subjective intelligibility measures, while having only minimal deviation (±0.01) across four different reference speaker pairs. We also demonstrate the robustness of the proposed method (R = −0.89 deviating ±0.02 over 1000 iterations) by considering a significantly smaller amount of utterances per speaker. Our results are among the first to show that disentangled speech representations can be used for automatic pathological speech intelligibility assessment, resulting in a reference speaker pair invariant method, applicable in scenarios with only few utterances available.<br
Keywords: auto-encoder; speech disentanglement; DTW; pathological speech intelligibility

Dissecting Glaciers - Can an Automated Bio-Medical Image Segmentation Tool also Segment Glaciers? - Gourmelon, Nora; Seehaus, Thorsten; Braun, Matthias; Maier, Andreas; Christlein, Vincent (EGU General Assembly 2022, 2022)

Distance error correction for time-of-flight cameras - Fürsattel, Peter; Schaller, Christian; Maier, Andreas; Riess, Christian (Automated Visual Inspection and Machine Vision II, 2017)
Keywords: Time-of-Flight; Systematic Errors; Random Forests

Distinguishing Progression and Radionecrosis in large progressive Brain Metastases after Stereotaxy by integrating Radiomics, Dose Radiomics and clinical Risk Factors - Sommer, Phillip; Huang, Yixing; Srinivasan, Aishwarya Lakshmi; Fischer, Stefan; Dörfler, Arnd; Schmidt, Manuel; Frey, Benjamin; Maier, Andreas; Fietkau, Rainer; Bert, Christoph; Putz, Florian (STRAHLENTHERAPIE UND ONKOLOGIE, 2022)
CRIS-Team WoS Importer:2022-06-03

DL-based inpainting for metal artifact reduction for cone beam CT using metal path length information - Gottschalk, Tristan; Maier, Andreas; Kordon, Florian Johannes; Kreher, Björn W. (Medical Physics, 2022)
Background: Metallic implants, which are inserted into the patient's body during trauma interventions, are the main cause of heavy artifacts in 3D X-ray acquisitions. These artifacts then hinder the evaluation of the correct implant's positioning, thus leading to a disturbed patient's healing process and increased revision rates. Purpose: This problem is tackled by so-called metal artifact reduction (MAR) methods. This paper examines possible advances in the inpainting process of such MAR methods to decrease disruptive artifacts while simultaneously preserving important anatomical structures adjacent to the inserted implants. Methods: In this paper, a learning-based inpainting method for cone-beam computed tomography is proposed that couples a convolutional neural network (CNN) with an estimated metal path length as prior knowledge. Further, the proposed method is solely trained and evaluated on real measured data. Results: The proposed inpainting approach shows advantages over the inpainting method used by the currently clinically approved frequency split metal artifact reduction (fsMAR) method as well as the learning-based state-of-the-art (SOTA) method PConv-Net. The major improvement of the proposed inpainting method lies in the ability to correctly preserve important anatomical structures in those regions adjacent to the metal implants. Especially these regions are highly important for a correct implant's positioning in an intraoperative setup. Using the proposed inpainting, the corresponding MAR volumes reach a mean structural similarity index measure (SSIM) score of 0.9974 and outperform the other methods by up to 6 dB on single slices regarding the peak signal-to-noise ratio (PSNR) score. Furthermore, it can be shown that the proposed method can generalize to clinical cases at hand. Conclusions: In this paper, a learning-based inpainting network is proposed that leverages prior knowledge about the metal path length of the inserted implant. Evaluations on real measured data reveal an increased overall MAR performance, especially regarding the preservation of anatomical structures adjacent to the inserted implants. Further evaluations suggest the ability of the proposed approach to generalize to clinical cases.
Created from Fastlane, Scopus look-up
Keywords: deep learning; metal artifact reduction; trauma interventions

DNN-based Speed-of-Sound Reconstruction for Automated Breast Ultrasound - Khun Jush, Farnaz; Biele, Markus; Dueppenbecker, Peter Michael; Schmidt, Oliver; Maier, Andreas (2020 IEEE International Ultrasonics Symposium (IUS), 2020)
The gold-standard for breast cancer screening is xray mammography. Alongside, ultrasound scans are being used as additional source of information for patients with dense breast tissue. However, conventional ultrasound imaging is a qualitative approach and is prone to errors. Quantitative approaches can provide valuable information about tissue properties, e.g. the speed-of-sound in the tissue can be used as a biomarker for breast tissue malignancy. Recent studies showed the possibility of speed-of-sound reconstruction from ultrasound raw data using Deep Neural Networks (DNNs). In this study, we investigate the feasibility of DNN-based speed-of-sound reconstruction for automated breast ultrasound with simulated and real data. We set up a DNN for speed-of-sound reconstruction. The network is fully trained on simulated data. Simulations are based on the LightABVS transducer, a linear transducer with 192 active channels. The input of the network is raw channel data from a single plane-wave acquisition. The output of the network is a speed-of-sound map with a resolution of 0.1 mm. We achieved Mean Absolute Percentage Error of 0.39 ± 0.03% and Root-Mean-Square Error of 14.85 ± 0.52 m/s on simulated dataset and promising results on real dataset which demonstrates great potential of this method for integration in conventional ultrasound systems.<br
Keywords: Speed-of-Sound; Deep Neural Networks; Breast Ultrasound

Dose Reduction Achieved by Dynamically Collimating the Redundant Rays in Fan-beam and Cone-beam CT - Xia, Yan; Berger, Martin; Riess, Christian; Hornegger, Joachim; Maier, Andreas (Proceedings of the IEEE NSS/MIC 2013, 2013)
In X-ray computed tomography (CT), short-scan acquisition that acquires data only over a range of π plus the fan angle, rather than a full range of 2π, is commonly used in circular fan-beam and cone-beam geometry. Such scan aims to reduce acquisition time and radiation dose during data acquisition. However, during a partial circle scan some data are measured once, while other measurements are observed twice. Traditionally, the redundant data are weighted by a smooth function (e.g. the Parker weights) before filtering. In this paper, we present an algorithmic setup that employs dynamic collimation to shield the redundant rays and propose two algorithms to correct the resulting truncation. This approach is able to potentially decrease the dose of 10% for a C-arm CT with fan angle of 10° and of 23% for a diagnostic CT with fan angle of 50°. Evaluation shows that the reconstruction results are of comparable accuracy to the one from standard short-scan FDK, with less dose to the patient. © 2013 IEE
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.dosere_7
Keywords: GRK-1773

Dose Reduction for Historical Books Digitization by 3-D X-Ray CT - Stromer, Daniel; Christlein, Vincent; Huang, Yixing; Zippert, Patrick; Helmecke, Eric; Hausotte, Tino; Maier, Andreas (Proceedings of 8th Conference on Industrial Computed Tomography (iCT 2018), 2018)
UnivIS-Import:2018-02-22:Pub.2018.tech.IMMD.IMMD5.dosere_1

Double Your Views: Exploiting Symmetry in Transmission Imaging - Preuhs, Alexander; Maier, Andreas; Manhart, Michael; Fotouhi, Javad; Navab, Nassir; Unberath, Mathias (Medical Image Computing and Computer Assisted Intervention, 2018)
CRIS-Team Scopus Importer:2019-05-14

Dual-Energy C-arm CT in the Angiographic Suite - Datta, Sanjit; Choi, Jang-Hwan; Niebler, Christine; Maier, Andreas; Fahrig, Rebecca; Müller, Kerstin (2015 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.dualen

Dual-Mode Training with Style Control and Quality Enhancement for Road Image Domain Adaptation - Venator, Moritz; Shen, Fengyi; Aklanoglu, Selcuk; Bruns, Erich; Diepold, Klaus; Maier, Andreas (2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020)
Dealing properly with different viewing conditions remains a key challenge for computer vision in autonomous driving. Domain adaptation has opened new possibilities for data augmentation, translating arbitrary road scene images into different environmental conditions. Although multimodal concepts have demonstrated the capability to separate content and style, we find that existing methods fail to reproduce scenes in the exact appearance given by a reference image. In this paper, we address the aforementioned problem by introducing a style alignment loss between output and reference image. We integrate this concept into a multimodal unsupervised image-to-image translation model with a novel dual-mode training process and additional adversarial losses. Focusing on road scene images, we evaluate our model in various aspects including visual quality and feature matching. Our experiments reveal that we are able to significantly improve both style alignment and image quality in different viewing conditions. Adapting concepts from neural style transfer, our new training approach allows to control the output of multimodal domain adaptation, making it possible to generate arbitrary scenes and viewing conditions for data augmentation.

Dynamic 2-D/3-D Rigid Registration Framework Using Point-To-Plane Correspondence Model - Wang, Jian; Schaffert, Roman; Borsdorf, Anja; Heigl, Benno; Huang, Xiaolin; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Medical Imaging, 2017)
In image-guided interventional procedures, live 2-D X-ray images can be augmented with preoperative 3-D computed tomography or MRI images to provide planning landmarks and enhanced spatial perception. An accurate alignment between the 3-D and 2-D images is a prerequisite for fusion applications. This paper presents a dynamic rigid 2-D/3-D registration framework, which measures the local 3-D-to-2-D misalignment and efficiently constrains the update of both planar and non-planar 3-D rigid transformations using a novel point-to-plane correspondence model. In the simulation evaluation, the proposed method achieved a mean 3-D accuracy of 0.07 mm for the head phantom and 0.05 mm for the thorax phantom using single-view X-ray images. In the evaluation on dynamic motion compensation, our method significantly increases the accuracy comparing with the baseline method. The proposed method is also evaluated on a publicly-available clinical angiogram data set with 'gold-standard' registrations. The proposed method achieved a mean 3-D accuracy below 0.8 mm and a mean 2-D accuracy below 0.3 mm using single-view X-ray images. It outperformed the state-of-the-art methods in both accuracy and robustness in single-view registration. The proposed method is intuitive, generic, and suitable for both initial and dynamic registration scenarios.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.dynami
Keywords: dynamic registration; point-to-plane correspondence model; Rigid 2-D/3-D registration

Dynamic detector offsets for field of view extension in C-arm computed tomography with application to weight-bearing imaging - Herbst, Magdalena; Schebesch, Frank; Berger, Martin; Choi, Jang-Hwan; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (Medical Physics, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.dynami
Keywords: GRK-1773

Dynamic Iterative Reconstruction for Interventional 4-D C-Arm CT Perfusion Imaging - Manhart, Michael; Kowarschik, Markus; Fieselmann, Andreas; Deuerling-Zheng, Yu; Royalty, Kevin; Maier, Andreas; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2013)

Dynamic Pixel Binning allows Spatial and Angular Resolution Tradeoffs to improve Image Quality in X-Ray C-Arm CT - Steg, Alexander; Reichenbach, Marc; Söll, Christopher; Shi, Lan; Maier, Andreas; Riess, Christian (IEEE International Symposium on Biomedical Imaging (ISBI), 2016)
When designing an acquisition protocol for a C-arm CT acquisition, a tradeoff has to be made between scan time and the amount of acquired data. If the scan is increased, data of spatially and angular higher resolution can be acquired. If the scan time is fixed, one has to tradeoff spatial and angular resolution, which may either lead to oversmoothing or artifacts from angular undersampling. In this work, we investigate the viability of smart cameras for pushing the boundary for this bottleneck considerably further. The detector pixels are enhanced by processing units that are able to perform a limited amount of local operations. This allows an online, dynamical adaptation of the pixel binning, depending on whether a local neighborhood is smooth or not. We demonstrate on phantom data that this approach allows to save about 75% of the data, obtaining a reconstruction quality comparable to 1 x 1 binning at a data rate comparable to 2 x 2 pixel binning.
elib2cris::1512001600,steg2016
Keywords: Pixel Binning; X-ray detector; C-arm reconstruction; Dynamic Pixel Binning; X-ray C-arm CT

Dynamic Reconstruction with Statistical Ray Weighting for C-Arm CT Perfusion Imaging - Manhart, Michael; Fieselmann, Andreas; Deuerling-Zheng, Yu; Maier, Andreas; Kowarschik, Markus (Proc. 12th Fully 3D, 2013)

Edge-Preserving Bilateral Filtering for Images Containing Dense Objects in CT - Yang, Qiao; Maier, Andreas; Maass, Nicole; Hornegger, Joachim (2013 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2013)
In computed tomography (CT), dense objects such as bone, metal implants, and contrast medium, induce cupping and streaking artifacts. To reduce those artifacts, various approaches have a common strategy, which is segmenting datasets into different materials and correcting them separately according to their own physical characteristics. However, in most cases, the severe artifacts hinder the primary segmentation which results in low efficiency of artifact reduction approaches. When taking noise under consideration, the accuracy of segmentation gets even worse. In this work, we applied an edge preserving step based on bilateral filtering between reconstruction and segmentation. A traditional bilateral filter performs noise reduction, and a bilateral edge detector exploits the structural edge information. By incorporating the edge information with noise reduced reconstruction images, a more sophisticated segmentation approach is proposed. Quantitative evaluations of noise reduction and segmentation performance are carried out using simulated and real CT datasets. The results show that our approach can reduce streak artifacts at a primary level, which significantly improves segmentation. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.edgepr_0
Keywords: Beam Hardening Correction; Computed Tomography; Image Segmentation; Noise Reduction

Editorial: Auditory perception and phantom perception in brains, minds and machines - Schilling, Achim; Schaette, Roland; Sedley, William; Gerum, Richard Carl; Maier, Andreas; Krauß, Patrick (Frontiers in Neuroscience, 2023)
CRIS-Team Scopus Importer:2023-11-03
Keywords: artificial intelligence; auditory neuroscience; auditory phantom perception; cognitive computational neuroscience; deep neural networks; hearing loss; neuroimaging; tinnitus

Editorial for the Special Issue on MICCAI 2015 - Navab, Nassir; Frangi, Alejandro F.; Wells, William; Maier, Andreas (Medical Image Analysis, 2016)
CRIS-Team Scopus Importer:2022-08-05

Effective One Step-iterative Fiducial Marker-based Compensation for Involuntary Motion in Weight-bearing C-arm Cone-beam CT Scanning of Knees - Choi, Jang-Hwan; Maier, Andreas; Berger, Martin; Fahrig, Rebecca (Proc. SPIE Medical Imaging 2014, 2014)
We previously introduced three different fiducial marker-based correction methods (2D projection shifting, 2D projection warping, and 3D image warping) for patientsâ€™involuntary motion in the lower body during weight-bearing Carm CT scanning. The 3D warping method performed better than 2D methods since it could more accurately take into account the lower body motion in 3D. However, as the 3D warping method applies different rotational and translational movement to the reconstructed image for each projection frame, distance-related weightings were slightly twisted and thus result in overlaying background noise over the entire image. In order to suppress background noise and artifacts (e.g. metallic marker-caused streaks), the 3D warping method has been improved by incorporating bilateral filtering and a Landwebertype iteration in one step. A series of projection images of five healthy volunteers standing at various flexion angles were acquired using a C-arm cone-beam CT system with a flat panel. A horizontal scanning trajectory of the C-arm was calibrated to generate projection matrices. Using the projection matrices, the static reference marker coordinates in 3D were estimated and used for the improved 3D warping method. The improved 3D warping method effectively reduced background noise down below the noise level of 2D methods and also eliminated metal-generated streaks. Thus, improved visibility of soft tissue structures (e.g. fat and muscle) was achieved while maintaining sharp edges at bone-tissue interfaces. Any high resolution weight-bearing cone-beam CT system can apply this method for motion compensation. © 2014 SPIE.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.effect
Keywords: GRK-1773

Effect of Data-Driven Respiratory Gating on Lu-177 SPECT/CT Scans - Sanders, James Chester; Kuwert, Torsten; Vija, A. Hans; Maier, Andreas; Ritt, Philipp (29th Annual EANM Congress of the European Association of Nuclear Medicine 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.effect_2

Effect of High-Uptake Regions with Inconsistent Motion on Data Driven Respiratory Gating in SPECT Myocardial Perfusion Imaging using Digital Phantoms - Reymann, M.; Massanes, F.; Kuwert, Torsten; Vija, A. H.; Maier, Andreas (EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2022)
CRIS-Team WoS Importer:2022-10-28

Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning - Bhandary Panambur, Adarsh; Madhu, Prathmesh; Maier, Andreas (German Workshop on Medical Image Computing, 2022, 2022)

Effects of functional endoscopic sinus surgery on the acoustics of the sinonasal tract - Havel, Miriam; Becker, Sven; Schuster, Maria; Johnson, Thorsten; Maier, Andreas; Sundberg, Johan (Rhinology, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.eectso
Keywords: 3D replica; Cadaveric study; Functional endoscopic sinus surgery; Paranasal sinuses; Resonance frequency; Sinonasal tract

Effects of Tissue Material Properties on X-Ray Image, Scatter and Patient Dose A Monte Carlo Simulation - Roser, Philipp; Birkhold, Annette; Zhong, Xia; Stepina, Elizaveta; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Informatik aktuell, 2019)
With increasing patient and staff X-ray radiation awareness, many efforts have been made to develop accurate patient dose estimation methods. To date, Monte Carlo (MC) simulations are considered golden standard to simulate the interaction of X-ray radiation with matter. However, sensitivity of MC simulation results to variations in the experimental or clinical setup of image guided interventional procedures are only limited studied. In particular, the impact of patient material compositions is poorly investigated. This is mainly due to the fact, that these methods are commonly validated in phantom studies utilizing a single anthropomorphic phantom. In this study, we therefore investigate the impact of patient material parameters mapping on the outcome of MC X-ray dose simulations. A computation phantom geometry is constructed and three different commonly used material composition mappings are applied. We used the MC toolkit Geant4 to simulate X-ray radiation in an interventional setup and compared the differences in dose deposition, scatter distributions and resulting X-ray images. The evaluation shows a discrepancy between different material composition mapping up to 20% concerning directly irradiated organs. These results highlight the need for standardization of material composition mapping for MC simulations in a clinical setup.
CRIS-Team Scopus Importer:2019-05-14

Effects of Vocal Aging on Fundamental Frequency and Formants - Mwangi, Samuel; Spiegl, Werner; Hönig, Florian Thomas; Haderlein, Tino; Maier, Andreas; Nöth, Elmar (Proceedings of the International Conference on Acoustics NAG/DAGA 2009, 2009)

Efficient 2D Filtering for Cone-beam VOI Reconstruction - Xia, Yan; Maier, Andreas; Dennerlein, Frank; Hofmann, Hannes; Hornegger, Joachim (2012 Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference, 2012)

Efficient and high accuracy 3-D OCT angiography motion correction in pathology - Ploner, Stefan; Kraus, Martin; Moult, Eric M.; Husvogt, Lennart; Schottenhamml, Julia; Yasin Alibhai, A.; Waheed, Nadia K.; Duker, Jay S.; Fujimoto, James G.; Maier, Andreas (Biomedical Optics Express, 2021)
We describe a novel method for non-rigid 3-D motion correction of orthogonally raster-scanned optical coherence tomography angiography volumes. This is the first approach that aligns predominantly axial structural features such as retinal layers as well as transverse angiographic vascular features in a joint optimization. Combined with orthogonal scanning and favorization of kinematically more plausible displacements, subpixel alignment and micrometer-scale distortion correction is achieved in all 3 dimensions. As no specific structures are segmented, the method is by design robust to pathologic changes. Furthermore, the method is designed for highly parallel implementation and short runtime, allowing its integration into clinical workflow even for high density or wide-field scans. We evaluated the algorithm with metrics related to clinically relevant features in an extensive quantitative evaluation based on 204 volumetric scans of 17 subjects, including patients with diverse pathologies and healthy controls. Using this method, we achieve state-of-the-art axial motion correction and show significant advances in both transverse co-alignment and distortion correction, especially in the subgroup with pathology.
CRIS-Team Scopus Importer:2021-01-08

Efficient Epipolar Consistency - Aichert, André; Breininger, Katharina; Köhler, Thomas; Maier, Andreas (Proceedings of the 4th CT Meeting, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.effici_3

Efficient Exploitation of Image Repetitions in MR Reconstruction - Gadjimuradov, Fasil; Benkert, Thomas; Nickel, Marcel Dominik; Maier, Andreas (Medical Imaging with Deep Learning, 2022)

Efficient Labeling of Optical Coherence Tomography Angiography Data using Eye Tracking - Husvogt, Lennart; Moult, Eric M.; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Bildverarbeitung für die Medizin 2018, 2018)
We implemented an approach for the efficient labeling of structural and angiographic optical coherence tomography (OCT) data using eye tracking. OCT is a non-invasive imaging technology, which provides volumetric data from scattering tissues in micrometer resolution. Due to its widespread use and noninvasiveness, clinicians acquire large amounts of volumetric ophthalmic data on a daily basis.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.effici_1
Keywords: labeling; eye tracking

Einflussfaktoren auf Sprechstoerungen bei T1 und T2 Plattenepithelcarcinomen der Mundhoehle - Schuster, Maria; Maier, Andreas; Kohler, Regina; Nöth, Elmar; Rosanowski, Frank; Eysholdt, Ulrich; Nkenke, Emeka (Aktuelle phoniatrische Aspekte, 2007)

Ein klinisch anwendbares Neuronales Netzwerk zur Klassifikation suspekter Läsionen in der Mamma-MRT - Ellmann, Stephan; Bielowski, Christian; Wenkel, Evelyn; Vesal, Sulaiman; Maier, Andreas; Uder, Michael; Bäuerle, Tobias (Fortschritte auf dem Gebiete der Röntgenstrahlen, 2018)
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.einkli

Electrophysiology Catheter Detection and Reconstruction from Two Views in Fluoroscopic Images - Hoffmann, Matthias; Brost, Alexander; Koch, Martin; Bourier, Felix; Maier, Andreas; Kurzidim, Klaus; Strobel, Norbert; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2015)
UnivIS-Import:2016-02-10:Pub.2015.tech.IMMD.IMMD5.electr

Empirical Scatter Correction using the Epipolar Consistency Condition - Hoffmann, Mathis; Würfl, Tobias; Maass, Nicole; Dennerlein, Frank; Aichert, André; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2018)
Scatter affects every computed tomography (CT) image. Calibration-free software scatter reduction methods have not been used extensively in practice. Recently, consistency conditions have been applied successfully to other artifact reduction problems in CT imaging. We propose a scatter reduction method, that uses an epipolar consistency condition (ECC) to estimate parameters of an additive scatter model. We evaluate our approach by comparing it with an image-based empirical scatter correction method (ESC) that uses the same scatter model. We show that it performs equally well onsimulated data. Further, ECC outperforms ESC regarding the computational load for the determination of the parameters, because ECC is formulated in projection domain such that no image reconstruction is necessary. While some restrictions might apply for the stability of ECC on measured data, no prior information needs to be formulated regarding the reconstructed image, like it is required with ES
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.empiri

Employing Graph Representations for Cell-Level Characterization of Melanoma MELC Samples - Rivera Monroy, Luis; Rist, Leonhard; Eberhardt, Martin; Ostalecki, Christian; Baur, Andreas; Vera, Julio; Breininger, Katharina; Maier, Andreas (2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023)
Histopathology imaging is crucial for the diagnosis and treatment of skin diseases. For this reason, computer-assisted approaches have gained popularity and shown promising results in tasks such as segmentation and classification of skin disorders. However, collecting essential data and sufficiently high-quality annotations is a challenge. This work describes a pipeline that uses suspected melanoma samples that have been characterized using Multi-Epitope-Ligand Cartography (MELC). This cellular level characterization of the tissue is then represented as a graph and finally used to train a graph neural network. This imaging technology, combined with the methodology proposed in this work, achieves a classification accuracy of 87 %, outperforming existing approaches by 10 %.<br

Enabling Geometry Aware Learning Through Differentiable Epipolar View Translation - Rohleder, Maximilian; Pradel, Charlotte; Wagner, Fabian; Thies, Mareike; Maul, Noah; Denzinger, Felix; Maier, Andreas; Kreher, Bjoern (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
Epipolar geometry is exploited in several applications in the field of Cone-Beam Computed Tomography (CBCT) imaging. By leveraging consistency conditions between multiple views of the same scene, motion artifacts can be minimized, the effects of beam hardening can be reduced, and segmentation masks can be refined. In this work, we explore the idea of enabling deep learning models to access the known geometrical relations between views. This implicit 3D information can potentially enhance various projection domain algorithms such as segmentation, detection, or inpainting. We introduce a differentiable feature translation operator, which uses available projection matrices to calculate and integrate over the epipolar line in a second view. As an example application, we evaluate the effects of the operator on the task of projection domain metal segmentation. By re-sampling a stack of projections into orthogonal view pairs, we segment each projection image jointly with a second view acquired roughly 90 ^° apart. The comparison with an equivalent single-view segmentation model reveals an improved segmentation performance of 0.95 over 0.91 measured by the dice coefficient. By providing an implementation of this operator as an open-access differentiable layer, we seek to enable future research.
CRIS-Team Scopus Importer:2023-11-03
Keywords: Cone-Beam Computed Tomography; Epipolar Geometry; Operator Learning

Encoding CNN Activations for Writer Recognition - Christlein, Vincent; Maier, Andreas (2018 13th IAPR International Workshop on Document Analysis Systems (DAS), 2018)
The encoding of local features is an essential part for writer identification and writer retrieval. While CNN activations have already been used as local features in related works, the encoding of these features has attracted little attention so far. In this work, we compare the established VLAD encoding with triangulation embedding. We further investigate generalized max pooling as an alternative to sum pooling and the impact of decorrelation and Exemplar SVMs. With these techniques, we set new standards on two publicly available datasets (ICDAR13, KHATT).
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.encodi
Keywords: CNN activation features; encoding; generalized max-pooling; triangulation embedding; writer identification; writer retrieval

Endoscopy - Haase, Sven; Maier, Andreas (Springer, 2018)
UnivIS-Import:2019-03-21:Pub.2018.tech.IMMD.IMMD5.endosc

Enhanced Crack Segmentation (eCS): A Reference Algorithm for Segmenting Cracks in Multicrystalline Silicon Solar Cells - Stromer, Daniel; Vetter, Andreas; Özkan, Hasan Can; Probst, Christian; Maier, Andreas (IEEE Journal of Photovoltaics, 2019)
The annually produced quantity of solar modules has steadily increased over the past decades. Rising production speeds and the associated high throughput of wafers, cells, and modules will make an automatized quality inspection mandatory. In the case of visual optical inspection, automatized quality control by using machine vision is already possible. To localize cracks in solar cells, luminescence imaging is used, where several approaches for an automatized inspection exist, but a standard solution for an automatized inspection algorithm is not yet available. This is, in particular, true for multicrystalline solar cells, where the grainy structures in the luminescence images are hard to distinguish from small cracks. Another obstacle in automatic crack analysis is that reference segmentation algorithms are generally not publicly available. Accordingly, a new algorithm can hardly be compared by ranking it to an existing standard. In this paper, we adapted the vesselness algorithm for automatic processing of electroluminescence images of multicrystalline silicon solar cells. Segmentation of cracks in multicrystalline solar cells with the proposed enhanced crack segmentation algorithm shows very promising results on the used database compared with three different commonly used approaches. Furthermore, the segmentation code is made publicly available, and we propose that this algorithm may serve as a reference algorithm, sparking further progress in automatized crack segmentation for multicrystalline silicon solar cells.
CRIS-Team Scopus Importer:2019-05-07
Keywords: Crack detection; crack segmentation; electroluminescence (EL) imaging; multicrystalline solar cell imaging; photovoltaic (PV)

Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks - Denck, Jonas; Guehring, Jens; Maier, Andreas; Rothgang, Eva (Journal of Imaging, 2021)

Enhancing Collaborative Road Scene Reconstruction with Unsupervised Domain Alignment - Venator, Moritz; Aklanoglu, Selcuk; Bruns, Erich; Maier, Andreas (Machine Vision and Applications, 2021)
Scene reconstruction and visual localization in dynamic environments such as street scenes are a challenge due to the lack of distinctive, stable keypoints. While learned convolutional features have proven to be robust to changes in viewing conditions, hand-crafted features still have advantages in distinctiveness and accuracy when applied to Structure from Motion. For collaborative reconstruction of road sections by a car fleet, we apply multimodal domain adaptation as a preprocessing step to align images in their appearance and enhance keypoint matching across viewing conditions while preserving the advantages of hand-crafted features. Training a Generative Adversarial Network for translations between different illumination and weather conditions, we evaluate both qualitative and quantitative aspects of domain adaptation and its impact on feature correspondences. Combined with a multi-feature discriminator, the model is optimized for synthesis of images which do not only improve feature matching but also exhibit a high visual quality. Evaluating the approach with a challenging multi-domain image dataset recorded in various road scenes on multiple test drives, we show that our approach outperforms other traditional and learning-based methods by improving completeness or accuracy of Structure from Motion with multimodal two-domain image collections in eight out of ten test scene
Keywords: Domain adaptation; Image-to-image translation; Structure from Motion; Scene reconstruction; Visual localization

Enhancing downstream classification of breast abnormalities in contrast enhanced spectral mammography using a neighborhood representation loss - Bhandary Panambur, Adarsh; Madhu, Prathmesh; Bayer, Siming; Maier, Andreas (Medical Imaging 2024: Computer-Aided Diagnosis, 2024)
Contrast-enhanced spectral mammography (CESM) has proven to be a beneficial tool for additional management and diagnosis of breast cancer in patients. Deep learning-based automated tools for analyzing CESM images and detecting abnormalities can be crucial in reducing the workload of radiologists. Even though self-supervised learning (SSL) can significantly influence the quality of learned representations resulting in increased classification performances, it requires large amounts of data often unavailable in the CESM domain. In this work, we propose a novel loss function named Difficulty-Weighted Neighborhood Representation (DWNR) utilizing the SSL-based representations designed explicitly for the downstream classification of the region of interest (ROI) in CESM images in a small dataset setting. We propose utilizing the neighborhood representations extracted from an SSL backbone to guide the loss function in learning robust representations of the dataset’s normal, benign, and malignant ROIs. We formulate the DWNR loss using a combination of a neighborhood representation-guided triplet (NR-triplet) loss and a difficulty-weighted cross-entropy (DWCE) loss. The NR-triplet loss leverages the nearest and farthest neighbor feature representations, extracted from a self-supervised backbone, as positive and negative samples. Meanwhile, the DWCE loss exploits the class information from the nearest neighbors to perform per-sample difficulty weighting. The experimental results on the publicly available CDD-CESM dataset demonstrate that the proposed DWNR loss outperforms traditional loss functions such as Cross-Entropy (CE) and Focal loss by a significant gain of more than 2% absolute mean F1-scores for both SSL-based and ImageNet-SSL-based transfer learning. In addition, we demonstrate the efficacy of employing a DWNR-based ROI classifier for comprehensive CESM whole image analysis in BI-RADS scoring and cancer diagnosis. The results reveal a gain in mean F1-scores of more than 1% in both tasks when using the DWNR loss models for CNN initialization, indicating the significance of the proposed method to whole image analysis. Based on our analysis, incorporating DWNR loss during transfer learning could positively impact automated breast cancer diagnosis using the CESM imaging modality. This novel loss function opens new possibilities for leveraging deep learning in CESM, particularly in scenarios with limited labeled data availability.<br
Keywords: Machine learning; Feature extraction; Image classification; Deep learning; Breast; Breast cancer; Image analysis

Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer - Kulyabin, Mikhail; Zhdanov, Aleksei; Dolganov, Anton; Ronkin, Mikhail; Borisov, Vasilii; Maier, Andreas (Sensors, 2023)

Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer Learning - Madhu, Prathmesh; Villar-Corrales, Angel; Kosti, Ronak Vijaypal; Bendschus, Torsten; Reinhardt, Corinna; Bell, Peter; Maier, Andreas; Christlein, Vincent (Journal of Computing and Cultural Heritage, 2022)
Human pose estimation (HPE) is a central part of understanding the visual narration and body movements of characters depicted in artwork collections, such as Greek vase paintings. Unfortunately, existing HPE methods do not generalise well across domains resulting in poorly recognised poses. Therefore, we propose a two step approach: (1) adapting a dataset of natural images of known person and pose annotations to the style of Greek vase paintings by means of image style-transfer. We introduce a perceptually-grounded style transfer training to enforce perceptual consistency. Then, we fine-tune the base model with this newly created dataset. We show that using style-transfer learning significantly improves the SOTA performance on unlabelled data by more than 6% mean average precision (mAP) as well as mean average recall (mAR). (2) To improve the already strong results further, we created a small dataset (ClassArch) consisting of ancient Greek vase paintings from the 6–5th century BCE with person and pose annotations. We show that fine-tuning on this data with a style-transferred model improves the performance further. In a thorough ablation study, we give a targeted analysis of the influence of style intensities, revealing that the model learns generic domain styles. Additionally, we provide a pose-based image retrieval to demonstrate the effectiveness of our method. The code and pretrained models can be found at https://github.com/angelvillar96/STLPose.<br

Environmental Adaptation with a Small Data Set of the Target Domain - Maier, Andreas; Haderlein, Tino; Nöth, Elmar (Text, Speech and Dialogue, 2006)

Epipolar consistency conditions for motion correction in weight-bearing imaging - Bier, Bastian; Aichert, André; Felsner, Lina; Unberath, Mathias; Levenstone, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (Bildverarbeitung für die Medizin 2017 Algorithmen Systeme Anwendungen (Bildverarbeitung für die Medizin 2017), 2017)

Epipolar Consistency Guided Beam Hardening Reduction - ECC² - Würfl, Tobias; Maass, Nicole; Dennerlein, Frank; Huang, Xiaolin; Maier, Andreas (Proceedings of the 14th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.epipol_8

Epipolar Consistency in Fluoroscopy for Image-Based Tracking - Aichert, André; Wang, Jian; Schaffert, Roman; Dörfler, Arnd; Hornegger, Joachim; Maier, Andreas (Proceedings of the British Machine Vision Conference 2015, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.epipol_3

Epipolar Consistency in Transmission Imaging - Aichert, André; Berger, Martin; Wang, Jian; Maass, Nicole; Dörfler, Arnd; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Medical Imaging, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.epipol
Keywords: X-rays; motion estimation; geometry; stereo vision; X-ray tomography.

Epithelial Cell Detection in Endomicroscopy Images of the Vocal Folds - Mualla, Firas; Schöll, Simon; Bohr, Christopher; Neumann, Helmut; Maier, Andreas (Springer Proceedings in Physics 154, Proceedings of InterM, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.epithe

Epoch-wise label attacks for robustness against label noise - Gündel, Sebastian; Maier, Andreas (Bildverarbeitung für die Medizin 2020, 2020)
The current accessibility to large medical datasets for training convolutional neural networks is tremendously high. The associated dataset labels are always considered to be the real "ground truth". However, the labeling procedures often seem to be inaccurate and many wrong labels are integrated. This may have fatal consequences on the performance of both training and evaluation. In this paper, we show the impact of label noise in the training set on a specific medical problem based on chest X-ray images. With a simple one-class problem, the classification of tuberculosis, we measure the performance on a clean evaluation set when training with label-corrupt data. We develop a method to compete with incorrectly labeled data during training by randomly attacking labels on individual epochs. The network tends to be robust when flipping correct labels for a single epoch and initiates a good step to the optimal minimum on the error surface when flipping noisy labels. On a baseline with an AUC (Area under Curve) score of 0.924, the performance drops to 0.809 when 30% of our training data is misclassified. With our approach the baseline performance could almost be maintained, the performance raised to 0.918.<br

Erlangen-CLP: A Large Annotated Corpus of Speech from Children with Cleft Lip and Palate - Bocklet, Tobias; Maier, Andreas; Riedhammer, Korbinian Thomas; Eysholdt, Ulrich; Nöth, Elmar (2014)
In this paper we describe Erlangen-CLP, a large speech database of children with Cleft Lip and Palate. More than 800 German children with CLP (most of them between 4 and 18 years old) and 380 age matched control speakers spoke the semi-standardized PLAKSS test that consists of words with all German phonemes in different positions. So far 250 CLP speakers were manually transcribed, 120 of these were analyzed by a speech therapist and 27 of them by four additional therapists. The tharapists marked 6 different processes/criteria like pharyngeal backing and hypernasality which typically occur in speech of people with CLP. We present detailed statistics about the the marked processes and the inter-rater agreement.
Keywords: Cleft lip and palate; pathologic speech; Children's speech

Erlernbarkeitsstudie eines vibrotaktilen Armbands für assistive Navigation - Calim, Hakan; Maier, Andreas (Informatik aktuell, 2020)
CRIS-Team Scopus Importer:2020-04-21

Estimate, Compensate, Iterate: Joint Motion Estimation and Compensation in 4-D Cardiac C-arm Computed Tomography - Taubmann, Oliver; Lauritsch, Günter; Maier, Andreas; Fahrig, Rebecca; Hornegger, Joachim (Medical Image Computing and Computer-Assisted Intervention MICCAI 2015, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.estima_9

Estimating follow-up CTs from geometric deformations of catheter implants in interstitial breast brachytherapy: A feasibility study using electromagnetic tracking - Dürrbeck, Christopher; Schulz, Moritz; Pflaum, Leonie; Kallis, Karoline; Geimer, Tobias; Abu-Hossin, Nadin; Strnad, Vratislav; Maier, Andreas; Fietkau, Rainer; Bert, Christoph (Medical Physics, 2023)
Background: Electromagnetic tracking (EMT) systems have been shown to provide valuable information on the geometry of catheter implants in breast cancer patients undergoing interstitial brachytherapy (iBT). In the context of an extended patient-specific, pre-treatment verification, EMT can play a key role in determining the potential need and, if applicable, the appropriate time for treatment adaptation. To detect dosimetric shortcomings the relative position between catheters, and target volume and critical structures must be known. Since EMT cannot provide the anatomical context and standard imaging techniques such as cone-beam CT are not yet available in most brachytherapy suites, it is not possible to detect anatomic changes on a daily or fraction basis, so the need for adaptive planning cannot be identified. Purpose: The aim of this feasibility study is to develop and evaluate a technique capable of estimating follow-up CTs at any time based on the initial treatment planning CT (PCT) and surrogate information about changes of the implant geometry from an EMT system. Methods: A deformation vector field is calculated from two different implant reconstructions acquired in treatment position through EMT, the first immediately after the PCT and the second at another time point during the course of treatment. The calculation is based on discrete displacement vectors of pairs of control and target points. These are extrapolated by means of different radial basis functions in order to cover the entire CT volume. The adequate parameters for the calculation of the deformation field were identified. By warping the PCT according to the deformation field, one obtains an estimated CT (ECT) that reflects the geometric changes. For the proof of concept, ECTs were computed for the time point of the clinical follow-up CT (FCT) that is embedded in the treatment workflow after the fourth fraction. Results: ECT and clinical FCTs of 20 patients were compared to each other quantitatively in terms of absolute Hounsfield unit differences in the planning target volume (PTV) and in a convex hull (CH) enclosing the catheters. The median differences were 31.2 and 29.5 HU for the CH and the PTV, respectively. Conclusion: The proposed ECT approach was able to approximate the “anatomy of the day” and therefore, in principle, allows a dosimetric appraisal of the treatment plan quality before each fraction. In this way, it can contribute to a more detailed patient-specific quality assurance in iBT of the breast and help to identify the timing for a potential treatment adaptation.
CRIS-Team Scopus Importer:2023-08-25
Keywords: assurance; breast cancer; electromagnetic tracking; interstitial brachytherapy

Estimating the Fundamental Matrix Without Point Correspondences WithApplication to Transmission Imaging - Würfl, Tobias; Aichert, André; Maass, Nicole; Dennerlein, Frank; Maier, Andreas (Proceedings of the IEEE International Conference on Computer Vision, 2019)
We present a general method to estimate the fundamental matrix from a pair of images under perspective projection without the need for image point correspondences. Our method is particularly well-suited for transmission imaging, where state-of-the-art feature detection and matching approaches generally do not perform well. Estimation of the fundamental matrix plays a central role in auto-calibration methods for reflection imaging. Such methods are currently not applicable to transmission imaging. Furthermore, our method extends an existing technique proposed for reflection imaging which potentially avoids the outlier-prone feature matching step from an orthographic projection model to a perspective model. Our method exploits the idea that under a linear attenuation model line integrals along corresponding epipolar lines are equal if we compute their derivatives in orthogonal direction to their common epipolar plane. We use the fundamental matrix to parametrize this equality. Our method estimates the matrix by formulating a non-convex optimization problem, minimizing an error in our measurement of this equality. We believe this technique will enable the application of the large body of work on image-based camera pose estimation to transmission imaging leading to more accurate and more general motion compensation and auto-calibration algorithms, particularly in medical X-ray and Computed Tomography imaging
Keywords: Structure from Motion; CT reconstruction

Estimation of convolution kernels for X-ray scatter signal correction Schätzung von Faltungskernen zur Röntgen-Streusignalkorrektur - Luckner, Christoph; Maier, Andreas; Dennerlein, Frank (Informatik aktuell, 2014)
CRIS-Team Scopus Importer:2022-08-05

Estimation of femoral artery access location for anatomic deformation correction - Breininger, Katharina; Pfister, Marcus; Koutouzi, Giasemi; Kowarschik, Markus; Maier, Andreas (3rd Conference on Image-Guided Interventions & Fokus Neuroradiologie, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.estima

Estimation of missing fan-beam projections using frequency consistency conditions - Pohlmann, Marcel; Berger, Martin; Maier, Andreas; 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.estima
Keywords: GRK-1773

Estimation of statistical weights for model-based iterative ct reconstruction - Haase, Viktor; Stierstorfer, K.; Hahn, K.; Schöndube, H.; Maier, Andreas; Noo, F. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2021)
Since the introduction of model-based iterative reconstruction for computed tomography (CT) by Thibault et al. in 2007, statistical weights play an important role in the problem formulation with the objective to improve image quality. Statistical weights depend on the variance of measurements. However, this variance is not known and therefore weights must be estimated. So far, the literature neither discusses how statistical weights should be estimated nor how accurate the estimation needs to be. Our submission aims at filling this gap in the literature. Specifically, we propose an estimation procedure for statistical weights and assess this procedure with real CT data. The estimated weights are compared against (clinically unpractical) sample weights obtained from repeated scans. The results show that the estimation procedure delivers reliable results for the rays that pass through the scanned object. Four imaging scenarios are considered; in each case the root mean square difference between the estimated and sample weights is below 5% of the maximum statistical weight value. When used for reconstruction, these differences are seen to have little impact: all voxel values within soft tissue (low contrast) regions differ by less than 1 HU. Our results demonstrate that the statistical weights can be sufficiently well estimated to closely approach the result that would be obtained if the weights were known.
CRIS-Team Scopus Importer:2021-04-16
Keywords: Computed tomography; Image quality; Model-based iterative reconstruction; Statistical weights

Estimation of the Source-Detector Alignment of Cone-Beam X-ray Systems using Collimator Edge Tracking - Luckner, Christoph; Mertelmeier, Thomas; Maier, Andreas; Ritschl, Ludwig (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.estima

Evaluation of geometry and size dependence for a polynomial water precorrection approach in C-arm computed tomography - Schönke, Daniel; Lietzmann, Florian; Maier, Andreas; Schad, Lothar R. (ECR 2015 Book of Abstracts - C - Scientific and Educational Exhibits, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.evalua
Keywords: Quality assurance; Computer Applications-General; Cone beam CT; Radiation physics

Evaluation of Interpolation Methods for Motion Compensated Tomographic Reconstruction for Cardiac Angiographic C-arm Data - Müller, Kerstin; Zheng, Yefeng; Lauritsch, Günter; Rohkohl, Christopher; Schwemmer, Chris; Maier, Andreas; Fahrig, Rebecca; Hornegger, Joachim (Proceedings of the second international conference on image formation in x-ray computed tomography, 2012)
http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2012/Mueller12-EOI.pdf

Evaluation of interpolation methods for surface-based motion compensated tomographic reconstruction for cardiac angiographic C-arm data - Müller, Kerstin; Schwemmer, Chris; Hornegger, Joachim; Zheng, Yefeng; Wang, Yang; Lauritsch, Günter; Rohkohl, Christopher; Maier, Andreas; Schultz, Carl; Fahrig, Rebecca (Medical Physics, 2013)
Purpose: For interventional cardiac procedures, anatomical and functional information about the cardiac chambers is of major interest. With the technology of angiographic C-arm systems it is possible to reconstruct intraprocedural three-dimensional (3D) images from 2D rotational angiographic projection data (C-arm CT). However, 3D reconstruction of a dynamic object is a fundamental problem in C-arm CT reconstruction. The 2D projections are acquired over a scan time of several seconds, thus the projection data show different states of the heart. A standard FDK reconstruction algorithm would use all acquired data for a filtered backprojection and result in a motion-blurred image. In this approach, a motion compensated reconstruction algorithm requiring knowledge of the 3D heart motion is used. The motion is estimated from a previously presented 3D dynamic surface model. This dynamic surface model results in a sparse motion vector field (MVF) defined at control points. In order to perform a motion compensated reconstruction, a dense motion vector field is required. The dense MVF is generated by interpolation of the sparse MVF. Therefore, the influence of different motion interpolation methods on the reconstructed image quality is evaluated. Methods: Four different interpolation methods, thin-plate splines (TPS), Shepard's method, a smoothed weighting function, and a simple averaging, were evaluated. The reconstruction quality was measured on phantom data, a porcine model as well as on in vivo clinical data sets. As a quality index, the 2D overlap of the forward projected motion compensated reconstructed ventricle and the segmented 2D ventricle blood pool was quantitatively measured with the Dice similarity coefficient and the mean deviation between extracted ventricle contours. For the phantom data set, the normalized root mean square error (nRMSE) and the universal quality index (UQI) were also evaluated in 3D image space. Results: The quantitative evaluation of all experiments showed that TPS interpolation provided the best results. The quantitative results in the phantom experiments showed comparable nRMSE of ≈0.047 ± 0.004 for the TPS and Shepard's method. Only slightly inferior results for the smoothed weighting function and the linear approach were achieved. The UQI resulted in a value of ≈ 99% for all four interpolation methods. On clinical human data sets, the best results were clearly obtained with the TPS interpolation. The mean contour deviation between the TPS reconstruction and the standard FDK reconstruction improved in the three human cases by 1.52, 1.34, and 1.55 mm. The Dice coefficient showed less sensitivity with respect to variations in the ventricle boundary. Conclusions: In this work, the influence of different motion interpolation methods on left ventricle motion compensated tomographic reconstructions was investigated. The best quantitative reconstruction results of a phantom, a porcine, and human clinical data sets were achieved with the TPS approach. In general, the framework of motion estimation using a surface model and motion interpolation to a dense MVF provides the ability for tomographic reconstruction using a motion compensation technique. © 2013 American Association of Physicists in Medicine.

Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge - Xiao, Yiming; Maier, Andreas; Wein, Wolfgang; Shams, Roozbeh; Kadoury, Samuel; Drobny, David; Modat, Marc; Reinertsen, Ingerid; Rivaz, Hassan; Chabanas, Matthieu; Fortin, Maryse; MacHado, Ines; et al. (IEEE transactions on medical imaging, 2020)
In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the pre-surgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging tool to track brain shift and tumor resection. Accurate image registration techniques that update pre-surgical MRI based on iUS are crucial but challenging. The MICCAI Challenge 2018 for Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018) provided a public platform to benchmark MRI-iUS registration algorithms on newly released clinical datasets. In this work, we present the data, setup, evaluation, and results of CuRIOUS 2018, which received 6 fully automated algorithms from leading academic and industrial research groups. All algorithms were first trained with the public RESECT database, and then ranked based on a test dataset of 10 additional cases with identical data curation and annotation protocols as the RESECT database. The article compares the results of all participating teams and discusses the insights gained from the challenge, as well as future work.
CRIS-Team Scopus Importer:2020-03-27
Keywords: brain; brain shift; MRI; Registration; tumor; ultrasound

Evaluation of Spectrum Mismatching using Spectrum Binning Approach for Statistical Polychromatic Reconstruction in CT - Yang, Qiao; Wu, Meng; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung für die Medizin, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.evalua_9

Evaluation of spectrum mismatching using spectrum binning for statistical polychromatic reconstruction in CT - Yang, Qiao; Wu, Meng; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Informatik aktuell, 2014)
In CT, the nonlinear attenuation characteristics of polychromatic X-rays cause beam hardening artifacts in the reconstructed images. Statistical algorithms can effectively correct beam hardening artifacts while providing the benefit of noise reduction. In practice, a big challenge for CT is the difficulty at acquiring accurate energy spectrum information, which hinders the efficiency of beam hardening correction approaches that require the spectrum as prior knowledge such as the statistical methods. In this paper, we used proposed energy spectrum binning approach for reducing prior knowledge from full spectrum to three energy bins to compare the results when applying parameters optimized for one spectrum to data measured using a different spectrum.
CRIS-Team Scopus Importer:2022-08-05

Evaluation of speech intelligibility for children with cleft lip and palate by means of automatic speech recognition - Schuster, Maria; Maier, Andreas; Haderlein, Tino; Nkenke, Emeka; Wohlleben, Ulrike; Rosanowski, Frank; Eysholdt, Ulrich; Nöth, Elmar (International Journal of Pediatric Otorhinolaryngology, 2006)

EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control - Marzahl, Christian; Aubreville, Marc; Bertram, Christof A.; Maier, Jennifer; Bergler, Christian; Kröger, Christine; Voigt, Jörn; Breininger, Katharina; Klopfleisch, Robert; Maier, Andreas (Scientific Reports, 2021)
In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.
CRIS-Team Scopus Importer:2021-04-09

Exhaustive Graph Cut-based Vasculature Reconstruction - Unberath, Mathias; Achenbach, Stephan; Fahrig, Rebecca; Maier, Andreas (Proceedings of the ISBI, 2016)
We propose an exhaustive extension to graph cut-based coronary artery reconstruction from multiple views of a rotational angiography sequence. The reconstruction is formulated as an energy minimization problem that is solved using the a-expansion algorithm. We enforce reprojection-based data consistency and completeness conditions on the reconstructed centerline. The proposed strategy omits the need for selection and manual refinement of a reference view. A phantom study is used to assess the performance in 2D and 3D. The average reprojection error decreased from 1.32 ± 0.99mm to 0.54 ± 0.02 mm. Moreover, an increase in Dice score from 0.50 ± 0.04 mm to 0.59 ± 0.00 mm was observed, indicating superior volumetric reconstruction quality. The results suggest that the proposed extension removes the susceptibility to reference frame selection and manual interaction, while increasing reconstruction quality.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.exhaus_2
Keywords: C-arm CT; Cardiac Dynamics; Cardiac Imaging; Epipolar Geometry; Graph Cut; Symmetrization

Exploring Epipolar Consistency Conditions for Rigid Motion Compensation in In-vivo X-ray Microscopy - Thies, Mareike; Wagner, Fabian; Gu, Mingxuan; Mei, Siyuan; Huang, Yixing; Pechmann, Sabrina; Aust, Oliver; Weidner, Daniela; Neag, Georgiana; Uderhardt, Stefan; Schett, Georg; Christiansen, Silke; et al. (Informatik aktuell, 2024)
Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are characteristic of osteoporosis. The complexity of this method stems from the requirement for high-quality 3D reconstructions of the murine bones. However, respiratory motion and muscle relaxation lead to inconsistencies in the projection data which result in artifacts in uncompensated reconstructions. Motion compensation using epipolar consistency conditions (ECC) has previously shown good performance in clinical CT settings. Here, we explore whether such algorithms are suitable for correcting motion-corrupted XRM data. Different rigid motion patterns are simulated and the quality of the motion-compensated reconstructions is assessed. The method is able to restore microscopic features for out-of-plane motion, but artifacts remain for more realistic motion patterns including all six degrees of freedom of rigid motion. Therefore, ECC is valuable for the initial alignment of the projection data followed by further fine-tuning of motion parameters using a reconstruction-based method.
CRIS-Team Scopus Importer:2024-03-29

Exploring GPT-4 as MR Sequence and Reconstruction Programming Assistant - Zaiss, Moritz; Rajput, Junaid R.; Dang, Hoai N.; Golkov, Vladimir; Cremers, Daniel; Knoll, Florian; Maier, Andreas (Informatik aktuell, 2024)
In this study, we explore the potential of generative pre-trained transformer (GPT), as a coding assistant for MRI sequence programming using the Pulseq framework. The programming of MRI sequences is traditionally a complex and time-consuming task, and the Pulseq standard has recently simplified this process. It allows researchers to define and generate complex pulse sequences used in MRI experiments. Leveraging GPT-4’s capabilities in natural language generation, we adapted it for MRI sequence programming, creating a specialized assistant named GPT4MR. Our tests involved generating various MRI sequences, revealing that GPT-4, guided by a tailored prompt, outperformed GPT-3.5, producing fewer errors and demonstrating improved reasoning. Despite limitations in handling complex sequences, GPT4MR corrected its own errors and successfully generated code with step-by-step instructions. The study showcases GPT4MR’s ability to accelerate MRI sequence development, even for novel ideas absent in its training set. While further research and improvement are needed to address complexity limitations, a well-designed prompt enhances performance. The findings propose GPT4MR as a valuable MRI sequence programming assistant, streamlining prototyping and development. The future prospect involves integrating a PyPulseq plugin into lightweight, open-source LLMs, potentially revolutionizing MRI sequence development and prototyping.
CRIS-Team Scopus Importer:2024-03-29

Exploring the Limitations of Hybrid Adiabatic Quantum Computing for Emission Tomography Reconstruction - Nau, Merlin; Vija, Alexander Hans; Gohn, Wesley; Reymann, Maximilian; Maier, Andreas (Journal of Imaging, 2023)
Our study explores the feasibility of quantum computing in emission tomography reconstruction, addressing a noisy ill-conditioned inverse problem. In current clinical practice, this is typically solved by iterative methods minimizing a L2 norm. After reviewing quantum computing principles, we propose the use of a commercially available quantum annealer and employ corresponding hybrid solvers, which combine quantum and classical computing to handle more significant problems. We demonstrate how to frame image reconstruction as a combinatorial optimization problem suited for these quantum annealers and hybrid systems. Using a toy problem, we analyze reconstructions of binary and integer-valued images with respect to their image size and compare them to conventional methods. Additionally, we test our method’s performance under noise and data underdetermination. In summary, our method demonstrates competitive performance with traditional algorithms for binary images up to an image size of 32 by 32 on the toy problem, even under noisy and underdetermined conditions. However, scalability challenges emerge as image size and pixel bit range increase, restricting hybrid quantum computing as a practical tool for emission tomography reconstruction until significant advancements are made to address this issu
Keywords: image reconstruction; quantum computing; quantum annealing; tomographic imaging; emission tomography

Exploring the space between smoothed and non-smooth total variation for 3D iterative CT reconstruction - Haase, Viktor; Stierstorfer, K.; Hahn, K.; Schöndube, H.; Maier, Andreas; Noo, F. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019)
Because total variation (TV) is non-differentiable, iterative reconstruction using a TV penalty comes with technical difficulties. To avoid these, it is popular to use a smooth approximation of TV instead, which is defined using a single parameter, herein called δ, with the convention that the approximation is better when δ is smaller. To our knowledge, it is not known how well image reconstruction with this approximation can approach a converged non-smooth TV-regularized result. In this work, we study this particular question in the context of X-ray computed tomography (CT). Experimental results are reported with real CT data of a head phantom and supplemented with a theoretical analysis. To address our question, we make proficient use of a generalized iterative soft-thresholding algorithm that allows us to handle TV and its smooth approximation in the same framework. Our results support the following conclusions. First, images reconstructed using the smooth approximation of TV appears to smoothly converge towards the TV result as δ tends to zero. Second, the value of δdoes not need to be overly small to obtain a result that is essentially equivalent to TV, implying that numerical instabilities can be avoided. Last, though it is smooth, the convergence with δ is not particularly fast, as the mean absolute pixel difference decreases only as √δ in our experiments. Altogether, we conclude that the approximation is a theoretically valid way to approximate the non-smooth TV penalty for CT, opening the door to safe utilization of a wide variety of optimization algorithms.
CRIS-Team Scopus Importer:2019-07-12
Keywords: Computed tomography; GISTA; Huber function; Iterative reconstruction; Total variation

Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation - Gündel, Sebastian; Setio, Arnaud; Grbic, Sasa; Maier, Andreas; Comaniciu, Dorin (Machine Learning in Medical Imaging, 2020)
Chest X-ray (CXR) is the most common examination for fast detection of pulmonary abnormalities. Recently, automated algorithms have been developed to classify multiple diseases and abnormalities in CXR scans. However, because of the limited availability of scans containing nodules and the subtle properties of nodules in CXRs, state-of-the-art methods do not perform well on nodule classification. To create additional data for the training process, standard augmentation techniques are applied. However, the variance introduced by these methods are limited as the images are typically modified globally. In this paper, we propose a method for local feature augmentation by extracting local nodule features using a generative inpainting network. The network is applied to generate realistic, healthy tissue and structures in patches containing nodules. The nodules are entirely removed in the inpainted representation. The extraction of the nodule features is processed by subtraction of the inpainted patch from the nodule patch. With arbitrary displacement of the extracted nodules in the lung area across different CXR scans and further local modifications during training, we significantly increase the nodule classification performance and outperform state-of-the-art augmentation methods.<br
Keywords: Nodule classification; Local feature augmentation; Context encoder; Chest X-ray

Extrapolation of Ventricular Activation Times From Sparse Electroanatomical Data Using Graph Convolutional Neural Networks - Meister, Felix; Passerini, Tiziano; Audigier, Chloé; Lluch, Èric; Mihalef, Viorel; Ashikaga, Hiroshi; Maier, Andreas; Halperin, Henry; Mansi, Tommaso (Frontiers in Physiology, 2021)
Electroanatomic mapping is the gold standard for the assessment of ventricular tachycardia. Acquiring high resolution electroanatomic maps is technically challenging and may require interpolation methods to obtain dense measurements. These methods, however, cannot recover activation times in the entire biventricular domain. This work investigates the use of graph convolutional neural networks to estimate biventricular activation times from sparse measurements. Our method is trained on more than 15,000 synthetic examples of realistic ventricular depolarization patterns generated by a computational electrophysiology model. Using geometries sampled from a statistical shape model of biventricular anatomy, diverse wave dynamics are induced by randomly sampling scar and border zone distributions, locations of initial activation, and tissue conduction velocities. Once trained, the method accurately reconstructs biventricular activation times in left-out synthetic simulations with a mean absolute error of 3.9 ms ± 4.2 ms at a sampling density of one measurement sample per cm2. The total activation time is matched with a mean error of 1.4 ms ± 1.4 ms. A significant decrease in errors is observed in all heart zones with an increased number of samples. Without re-training, the network is further evaluated on two datasets: (1) an in-house dataset comprising four ischemic porcine hearts with dense endocardial activation maps; (2) the CRT-EPIGGY19 challenge data comprising endo- and epicardial measurements of 5 infarcted and 6 non-infarcted swines. In both setups the neural network recovers biventricular activation times with a mean absolute error of less than 10 ms even when providing only a subset of endocardial measurements as input. Furthermore, we present a simple approach to suggest new measurement locations in real-time based on the estimated uncertainty of the graph network predictions. The model-guided selection of measurement locations allows to reduce by 40% the number of measurements required in a random sampling strategy, while achieving the same prediction error. In all the tested scenarios, the proposed approach estimates biventricular activation times with comparable or better performance than a personalized computational model and significant runtime advantages.
Keywords: deep learning; graph convolutional networks; cardiac computational modeling; electroanatomic mapping; sparse measurements

Factors affecting accuracy of S values and determination of time-integrated activity in clinical Lu-177 dosimetry - Götz, Th I.; Schmidkonz, Christian; Lang, E. W.; Maier, Andreas; Kuwert, Torsten; Ritt, Philipp (Annals of Nuclear Medicine, 2019)
Introduction: In any radiotherapy, the absorbed dose needs to be estimated based on two factors, the time-integrated activity of the administered radiopharmaceutical and the patient-specific dose kernel. In this study, we consider the uncertainty with which such absorbed dose estimation can be achieved in a clinical environment. Methods: To calculate the total error of dose estimation we considered the following aspects: The error resulting from computing the time-integrated activity, the difference between the S-value and the patient specific full Monte Carlo simulation, the error from segmenting the volume-of-interest (kidney) and the intrinsic error of the activimeter. Results: The total relative error in dose estimation can amount to 25.0% and is composed of the error of the time-integrated activity 17.1%, the error of the S-value 16.7%, the segmentation error 5.4% and the activimeter accuracy 5.0%. Conclusion: Errors from estimating the time-integrated activity and approximations applied to dose kernel computations contribute about equally and represent the dominant contributions far exceeding the contributions from VOI segmentation and activimeter accuracy.
CRIS-Team Scopus Importer:2019-06-18
Keywords: Lu-177 dosimetry; Monte Carlo simulations; S-values; SPECT/CT; Time integrated activity

Failure and Risk Analysis based on Maintenance Reports of Machines Components in Manufacturing Industry - Thamm, Aleksandra; Herz, Matthias; Wiedemann, Markus; Thamm, Florian; Maier, Andreas (XIII. International Conference on the Theory of Machines and Mechanisms - TMM2020, 2021)
Manufacturing engineering is concerned with the application of engineering practices in manufacturing processes and production methods. Continuous optimization of processes, tools as well as machinery and equipment is the key to producing quality products with optimal capital investment. Hereby, maintenance plays a crucial role and allows manufacturers to optimize their processes. To enable improvements in this field it is required to analyze and evaluate the necessities for actions on component level. In this work we want to present a detailed retrospective analysis of more than 4.000 maintenance reports and analyze causes for assembly failures depending on the associated components. Furthermore, we extend the analysis and investigate the failure dependency with respect to the days elapsed from the date of maintenance request to resolving the issue. It turned out, not all cases with high failure rates are associated with subsequent higher risks considering the respective urgency related to each failure case. Furthermore, we discovered especially hydraulic and pneumatic components have a significant high to critical risk, as the labour safety and other components are threatened by their failure. Moreover we determined, that only 10% of all actions were planned maintenance and thus we propose based on the results of the risks analysis, how planned and preventive maintenance should be extended to reduce costs in this regard.<br
Keywords: Manufacturing; Failure Analysis; Risk Analysis; Spare Parts; Maintenance; Preventive Maintenance

Fan-beam Projection Image Acquisition using MRI - Syben-Leisner, Christopher; Stimpel, Bernhard; Leghissa, Martino; Dörfler, Arnd; Maier, Andreas (3rd Conference on Image-Guided Interventions & Fokus Neuroradiologie, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fanbea_5

Fan-to-Parallel Beam Conversion: Deriving Neural Network Architectures Using Precision Learning - Syben-Leisner, Christopher; Stimpel, Bernhard; Lommen, Jonathan; Würfl, Tobias; Dörfler, Arnd; Maier, Andreas (Informatik aktuell, 2019)
CRIS-Team Scopus Importer:2019-05-14

Fast 3D YOLOv3 based standard plane regression of vertebral bodies in intra-operative CBCT volumes - Doerrich, Sebastian; Kordon, Florian Johannes; Denzinger, Felix; El Barbari, Jan S.; Privalov, Maxim; Vetter, Sven Y.; Maier, Andreas; Kunze, Holger (Journal of Medical Imaging, 2023)
Purpose Mobile C-arm systems represent the standard imaging devices within the field of spine surgery. In addition to 2D imaging, they allow for 3D scans while preserving unrestricted patient access. For viewing, the acquired volumes are adjusted such that their anatomical standard planes align with the axes of the viewing modality. This difficult and time-consuming step is currently performed manually by the leading surgeon. This process is automatized within this work to improve the usability of C-arm systems. Thereby, the spinal region consisting of multiple vertebrae and the standard planes of all vertebrae being of interest to the surgeon need to be taken into account. Approach An object detection algorithm based on the you only look once version 3 architecture, adapted to 3D inputs, is compared with a segmentation-based approach employing a 3D U-Net. Both algorithms are trained on a dataset of 440 and tested on 218 spinal volumes. Results Although the detection-based algorithm is slightly inferior concerning the detection (91% versus 97% accuracy), localization (1.26 mm versus 0.74 mm error) and alignment accuracy (5.00 deg versus 4.73 deg error), it outperforms the segmentation-based one in terms of speed (5 s versus 38 s). Conclusions Both algorithms show similar good results. However, the speed gain of the detection-based algorithm, resulting in a run time of 5 s, makes it more suitable for usage in an intra-operative scenari

Fast Adaptive Regularization for Perfusion Parameter Computation - Manhart, Michael; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Bildverarbeitung für die Medizin, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.fastad_9

Fast adaptive regularization for perfusion parameter computation: Tuning the Tikhonov regularization parameter to the SNR by regression - Manhart, Michael; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Informatik aktuell, 2015)
Computation of perfusion parameters by deconvolution from contrast-enhanced time-resolved CT or MR perfusion data sets is an illconditioned problem. Thus, adequate regularization and determination of corresponding regularization parameters is required. We present a novel method for Tikhonov regularization for perfusion imaging to locally adapt parameters to the SNR level by using a regression function. In an numerical evaluation our simple approach provided similar or even superior results compared to methods applying computationally more demanding L-curve analysis.
CRIS-Team Scopus Importer:2022-08-05

Fast and Efficient Limited Data Hyperspectral Remote Sensing Image Classification via GMM-Based Synthetic Samples - Davari, Amirabbas; Özkan, Hasan Can; Maier, Andreas; Rieß, Christian (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019)

Fast and Robust Detection of Solar Modules in Electroluminescence Images - Hoffmann, Mathis; Doll, Bernd; Talkenberg, Florian; Brabec, Christoph; Maier, Andreas; Christlein, Vincent (Computer Analysis of Images and Patterns, 2019)

Fast and robust selection of highly-correlated features in regression problems - Maier, Andreas; Rodriguez Salas, Dalia (2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), 2017)
Feature selection for regression problems can be highly beneficial in terms of robustness and execution speed. The Correlation-based Feature Selection (CFS) algorithm, in the attempt to find the best feature subset, evaluates different subsets and selects the one with the highest “goodness”. Such goodness is based on the co-relation between the addition of all features in the subset with the output variable. However, such a simple addition assumes that all features have the same weight in the output variable. This, in turn, assumes that all features are uncorrelated with each other. By considering an optimal weighting instead, a more robust measurement of the goodness can be obtained; however, such weighting is computationally expensive. In this paper, a feature selection algorithm named “R-fast” which considers the β coefficients in the standardized linear regression model as optimal weights is proposed. We also present a technique to quickly estimate approximations to the β coefficients. Our algorithm was evaluated using Multiple Regression Analysis (MRA) over 8 synthetic and 10 real-world datasets. Results show that, when selecting features with our proposed algorithm, MRA's performance is better than or equal to those obtained when selecting features with others well-known filter algorithms and when no selection is performed.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fastan
Keywords: Feature Selection; Regression Analysis

Fast Automatic Liver Tumor Radiofrequency Ablation Planning via Learned Physics Model - Meister, Felix; Audigier, Chloé; Passerini, Tiziano; Lluch, Èric; Mihalef, Viorel; Maier, Andreas; Tommaso, Mansi (25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VII, 2022)
Radiofrequency ablation is a minimally-invasive therapy recommended for the treatment of primary and secondary liver cancer in early stages and when resection or transplantation is not feasible. To significantly reduce chances of local recurrences, accurate planning is required, which aims at finding a safe and feasible needle trajectory to an optimal electrode position achieving full coverage of the tumor as well as a safety margin. Computer-assisted algorithms, as an alternative to the time-consuming manual planning performed by the clinicians, commonly neglect the underlying physiology and rely on simplified, spherical or ellipsoidal ablation estimates. To drastically speed up biophysical simulations and enable patient-specific ablation planning, this work investigates the use of non-autoregressive operator learning. The proposed architecture, trained on 1,800 biophysics-based simulations, is able to match the heat distribution computed by a finite-difference solver with a root mean squared error of 0.51 ± .50 ∘C and the estimated ablation zone with a mean dice score of 0.93 ± 0.05, while being over 100 times faster. When applied to single electrode automatic ablation planning on retrospective clinical data, our method achieves patient-specific results in less than 4 mins and closely matches the finite-difference-based planning, while being at least one order of magnitude faster. Run times are comparable to those of sphere-based planning while accounting for the perfusion of liver tissue and the heat sink effect of large vessels.<br

Fast Epipolar Consistency without the Need for Pseudo Matrix Inverses - Preuhs, Alexander; Manhart, Michael; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.fastep

Fast Interpolation of Dense Motion Fields from Synthetic Phantoms - Maier, Andreas; Taubmann, Oliver; Wetzl, Jens; Wasza, Jakob; Forman, Christoph; Fischer, Peter; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung für die Medizin 2014, 2014)
Numerical phantoms are a common tool for the evaluation of registration and reconstruction algorithms. For applications concerning motion, dense deformation fields are of particular interest. Phantoms, however, are often described as surfaces and thus motion vectors can only be generated at these surfaces. In order to create dense motion fields, interpolation is required. A frequently used method for this purpose is the Parzen interpolator. However, with a high number of surface motion vectors and a high voxel count, its run time increases dramatically. In this paper, we investigate different methods to accelerate the creation of these motion fields using hierarchical sampling and the random ball cover. In the results, we show that a 643 volume can be sampled in less than one second with an error below 0.1 mm. Furthermore, we accelerate the interpolation of a 2563 dense deformation field to only 6.5 minutes using the proposed methods from days with previous methods.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.fastin_7
Keywords: interpolation; motion field; phantom

Fast online-customized (FOCUS) parallel transmission pulses: A combination of universal pulses and individual optimization - Herrler, Jürgen; Liebig, Patrick; Gumbrecht, Rene; Ritter, Dieter; Schmitter, Sebastian; Maier, Andreas; Schmidt, Manuel; Uder, Michael; Dörfler, Arnd; Nagel, Armin Michael (Magnetic Resonance in Medicine, 2021)
Purpose: To mitigate spatial flip angle (FA) variations under strict specific absorption rate (SAR) constraints for ultra-high field MRI using a combination of universal parallel transmit (pTx) pulses and fast subject-specific optimization. Methods: Data sets consisting of B0, (Formula presented.) maps, and virtual observation point (VOP) data were acquired from 72 subjects (study groups of 48/12 healthy Europeans/Asians and 12 Europeans with pathological or incidental findings) using an 8Tx/32Rx head coil on a 7T whole-body MR system. Combined optimization values (COV) were defined as combination of spiral-nonselective (SPINS) trajectory parameters and an energy regularization weight. A set of COV was optimized universally by simulating the individual RF pulse optimizations of 12 training data sets (healthy Europeans). Subsequently, corresponding universal pulses (UPs) were calculated. Using COV and UPs, individually optimized pulses (IOPs) were calculated during the sequence preparation phase (maximum 15 s). Two different UPs and IOPs were evaluated by calculating their normalized root-mean-square error (NRMSE) of the FA and SAR in simulations of all data sets. Seven additional subjects were examined using an MPRAGE sequence that uses the designed pTx excitation pulses and a conventional adiabatic inversion. Results: All pTx pulses resulted in decreased mean NRMSE compared to a circularly polarized (CP) pulse (CP = 28%, UPs = 17%, and IOPs = 12%). UPs and IOPs improved homogeneity for all subjects. Differences in NRMSE between study groups were much lower than differences between different pulse types. Conclusion: UPs can be used to generate fast online-customized (FOCUS) pulses gaining lower NRMSE and/or lower SAR values.
CRIS-Team Scopus Importer:2021-01-15
Keywords: 7 T; k; parallel transmission (pTx); SPINS; ultra-high-field MRI; universal pulses

Fast Pose Verification for High-Speed Radiation Therapy - Maier, Andreas; Westphal, Susanne; Geimer, Tobias; Maxim, Peter G.; King, Gregory; Schueler, Emil; Fahrig, Rebecca; Loo, Billy (Bildverarbeitung für die Medizin 2017: Algorithmen - Systeme - Anwendungen, 2017)
This paper discusses fast pose verification for radiation therapy on a new high-speed radiation therapy device. The PHASER system follows the idea of 4th generation CT imaging and allows fast 360° treatment using a steerable electron beam. Doing so, dose delivery is possible in few seconds. A major problem, however, is fast verification of the patient pose during treatment. In this paper, we suggest to use a projection-based approach that can be evaluated quickly and allows an accuracy below 1 mm as shown by our simulation study based on planning data from six 4D CT data sets.
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.fastpo

Fast, Robust and Efficient Extraction of Book Pages from a 3-D X-ray CT Volume - Stromer, Daniel; Christlein, Vincent; Schön, Tobias; Holub, Wolfgang; Maier, Andreas (Proceedings of the 14th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fastro

Fast Sample Generation with Variational Bayesian for Limited Data Hyperspectral Image Classification - Davari, Amirabbas; Özkan, Hasan Can; Maier, Andreas; Riess, Christian (IEEE International Geoscience and Remote Sensing Symposium, 2018)

Fast simulation of x-ray projections of spline-based surfaces using an append buffer - Maier, Andreas; Hofmann, Hannes; Schwemmer, Chris; Hornegger, Joachim; Keil, Andreas; Fahrig, Rebecca (Physics in Medicine and Biology, 2012)
Many scientists in the field of x-ray imaging rely on the simulation of x-ray images. As the phantom models become more and more realistic, their projection requires high computational effort. Since x-ray images are based on transmission, many standard graphics acceleration algorithms cannot be applied to this task. However, if adapted properly, the simulation speed can be increased dramatically using state-of-the-art graphics hardware. A custom graphics pipeline that simulates transmission projections for tomographic reconstruction was implemented based on moving spline surface models. All steps from tessellation of the splines, projection onto the detector and drawing are implemented in OpenCL. We introduced a special append buffer for increased performance in order to store the intersections with the scene for every ray. Intersections are then sorted and resolved to materials. Lastly, an absorption model is evaluated to yield an absorption value for each projection pixel. Projection of a moving spline structure is fast and accurate. Projections of size 640×480 can be generated within 254ms. Reconstructions using the projections show errors below 1 HU with a sharp reconstruction kernel. Traditional GPU-based acceleration schemes are not suitable for our reconstruction task. Even in the absence of noise, they result in errors up to 9HU on average, although projection images appear to be correct under visual examination. Projections generated with our new method are suitable for the validation of novel CT reconstruction algorithms. For complex simulations, such as the evaluation of motion-compensated reconstruction algorithms, this kind of x-ray simulation will reduce the computation time dramatically. © 2012 Institute of Physics and Engineering in Medicine.

Fat Content and Fatty Acid Composition Quantification Using a 3D Stack-of-Radial Trajectory With Adaptive Gradient Calibration - Schneider, Manuel; Lugauer, Felix; Hoppe, Elisabeth; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.fatcon

Fat-Saturated MR Image Synthesis with Acquisition Parameter-Conditioned Image-to-Image Generative Adversarial Network - Denck, Jonas; Guehring, Jens; Maier, Andreas; Rothgang, Eva (International Society for Magnetic Resonance in Medicine (ISMRM) 29th Annual Meeting & Exhibition, 2021)

Fault and anomaly detection in district heating substations: A survey on methodology and data sets - Neumayer, Martin; Stecher, Dominik; Grimm, Sebastian; Maier, Andreas; Bücker, Dominikus; Schmidt, Jochen (Energy, 2023)
District heating systems are essential building blocks for affordable, low-carbon heat supply. Early detection and elimination of faults is crucial for the efficiency of these systems and necessary to achieve the low temperatures targeted for 4th generation district heating systems. Especially methods for fault and anomaly detection in district heating substations are currently of high interest, as faults in substations can be repaired quickly and inexpensively, and smart meter data are becoming widely available. In this paper, we review recent scientific publications presenting data-driven approaches for fault and anomaly detection in district heating substations with a focus on methods and data sets. Our review indicates that researchers use a wide variety of methods, mostly focusing on unsupervised anomaly detection rather than fault detection. This is due to a lack of labeled data sets, preventing the use of supervised learning methods and quantitative analysis. Together with the lack of publicly available data sets, this impedes the accurate comparison of individual methods. To overcome this impediment, increase the comparability of different methods and foster competition, future research should focus on establishing publicly available data sets, and industry-relevant metrics as benchmarks.
CRIS-Team Scopus Importer:2023-05-05
Keywords: District heating systems; Fault/anomaly detection; Machine learning

Feasibility of intraoperative assessment of safe surgical margins during laryngectomy with confocal laser endomicroscopy: A pilot study - Sievert, Matti; Oetter, Nicolai; Aubreville, Marc; Stelzle, Florian; Maier, Andreas; Eckstein, Markus; Mantsopoulos, Konstantinos; Gostian, Antoniu Oreste; Mueller, Sarina K.; Koch, Michael; Agaimy, Abbas; Iro, Heinrich; et al. (Auris Nasus Larynx, 2021)
Objective: This pilot study aimed to assess the feasibility of intraoperative assessment of safe margins with Confocal Laser Endomicroscopy (CLE) during planned partial or total laryngectomy. Methods: Eight patients with confirmed larynx squamous cell carcinoma (SCC) and planned partial or total laryngectomy were included in this study in March 2020. Two head and neck surgeons and one pathologist were asked to classify carcinoma or healthy epithelium in a sample of 94 representative sequences (5.640 images), blinded to the histological results (H&E staining). Results: Healthy mucosa areas showed epithelium with cells of uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. CLE optical biopsy of SCC demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, PPV, and NPV of 80.1%, 72.3%, 87.9%, 85.7%, and 76.1%, respectively. A distinct transition between healthy appearing tissue and suspicious lesions could also be detected. Conclusion: CLE can be easily integrated into the intraoperative setting, generate real-time, in-vivo microscopic images of the larynx for evaluation and demarcation of cancer. If validated in further studies, CLE could eventually contribute to a less radical approach by enabling a more precise evaluation of the cancer margin.
CRIS-Team Scopus Importer:2021-01-29
Keywords: Confocal laser endomicroscopy; Laryngeal cancer; Safe surgical margins

Feasibility of Motion Compensation using Inertial Measurement in C-arm CT - Maier, Jennifer; Aichert, André; Mehringer, Wolfgang; Bier, Bastian; Eskofier, Björn; Levenston, Marc; Gold, Garry; Fahrig, Rebecca; Bonaretti, Serena; Maier, Andreas (Conference Record of the 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2018)

Feasibility Study: 2-D Self-Navigation using Compressed Sensing Reconstruction for Respiratory Gating in Free-breathing 3-D CINE Imaging - Prochaska, Ivo; Wetzl, Jens; Forman, Christoph; Nagel, Armin Michael; Maier, Andreas (Proceedings of the 25th Annual Meeting of the ISMRM, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.feasib

Feasibility Study: Free-Breathing 3-D CINE Imaging with Respiratory Gating Based on Pilot Tone Navigation - Wetzl, Jens; Schroeder, Lea; Forman, Christoph; Lugauer, Felix; Rehner, Robert; Fenchel, Matthias; Maier, Andreas; Hornegger, Joachim; Speier, Peter (Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.feasib

Feature Loss After Denoising of SPECT Projection Data using a U-Net - Reymann, Maximilian; Massanes, Francesc; Ritt, Philipp; Cachovan, Michal; Kuwert, Torsten; Vija, A. Hans; Maier, Andreas (Feature Loss After Denoising of SPECT Projection Data using a U-Net, 2020)
It has been long known that denoising of SPECT projection data from multi channel collimators harbors the danger that features are filtered away as distinction between noise and signal in the 2D projection image does not consider the tomographic information (see Vija, Yahil et al.). We investigate if an approach using Neural Networks can overcome the past issues, by training a U-Net on Monte Carlo Simulations of the XCAT phantom. Although error metrics indicate improvement of signal statistics, we observe a loss of image features after the reconstruction. We show that when using OSEM reconstruction without denoising in projection domain, these features can be preserved.<br
Keywords: SPECT; Denoising; Monte Carlo Simulation; XCAT Phantom; SIMIND; U-Net

Federated learning for secure development of AI models for Parkinson’s disease detection using speech from different languages - Tayebi Arasteh, Soroosh; Rios-Urrego, Cristian David; Nöth, Elmar; Maier, Andreas; Yang, Seung Hee; Rusz, Jan; Rafael Orozco-Arroyave, Juan (Proceedings of INTERSPEECH 2023, 2023)
Parkinson's disease (PD) is a neurological disorder impacting a person's speech. Among automatic PD assessment methods, deep learning models have gained particular interest. Recently, the community has explored cross-pathology and cross-language models which can improve diagnostic accuracy even further. However, strict patient data privacy regulations largely prevent institutions from sharing patient speech data with each other. In this paper, we employ federated learning (FL) for PD detection using speech signals from 3 real-world language corpora of German, Spanish, and Czech, each from a separate institution. Our results indicate that the FL model outperforms all the local models in terms of diagnostic accuracy, while not performing very differently from the model based on centrally combined training sets, with the advantage of not requiring any data sharing among collaborators. This will simplify inter-institutional collaborations, resulting in enhancement of patient outcomes.
Keywords: federated learning; speech pathology; Parkinson’s disease; deep learning; trustworthy speech processing

Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. II. Experiment - Choi, Jang-Hwan; Maier, Andreas; Keil, Andreas; Pal, Saikat; McWalter, Emily; Beaupre, Gary; Gold, Garry; Fahrig, Rebecca (Medical Physics, 2014)
Purpose: A C-arm CT system has been shown to be capable of scanning a single cadaver leg under loaded conditions by virtue of its highly flexible acquisition trajectories. In Part I of this study, using the 4D XCAT-based numerical simulation, the authors predicted that the involuntary motion in the lower body of subjects in weight-bearing positions would seriously degrade image quality and the authors suggested three motion compensation methods by which the reconstructions could be corrected to provide diagnostic image quality. Here, the authors demonstrate that a flat-panel angiography system is appropriate for scanning both legs of subjectsin vivo under weight-bearing conditions and further evaluate the three motion-correction algorithms using in vivo data. Methods: The geometry of a C-arm CT system for a horizontal scan trajectory was calibrated using the PDS-2 phantom. The authors acquired images of two healthy volunteers while lying supine on a table, standing, and squatting at several knee flexion angles. In order to identify the involuntary motion of the lower body, nine 1-mm-diameter tantalum fiducial markers were attached around the knee. The static mean marker position in 3D, a reference for motion compensation, was estimated by back-projecting detected markers in multiple projections using calibrated projection matrices and identifying the intersection points in 3D of the back-projected rays. Motion was corrected using three different methods (described in detail previously): (1) 2D projection shifting, (2) 2D deformable projection warping, and (3) 3D rigid body warping. For quantitative image quality analysis, SSIM indices for the three methods were compared using the supine data as a ground truth. Results: A 2D Euclidean distance-based metric of subjects' motion ranged from 0.85 mm (±0.49 mm) to 3.82 mm (±2.91 mm) (corresponding to 2.76 to 12.41 pixels) resulting in severe motion artifacts in 3D reconstructions. Shifting in 2D, 2D warping, and 3D warping improved the SSIM in the central slice by 20.22%, 16.83%, and 25.77% in the data with the largest motion among the five datasets (SCAN5); improvement in off-center slices was 18.94%, 29.14%, and 36.08%, respectively. Conclusions: The authors showed that C-arm CT control can be implemented for nonstandard horizontal trajectories which enabled us to scan and successfully reconstruct both legs of volunteers in weight-bearing positions. As predicted using theoretical models, the proposed motion correction methods improved image quality by reducing motion artifacts in reconstructions; 3D warping performed better than the 2D methods, especially in off-center slices. © 2014 American Association of Physicists in Medicine.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.fiduci
Keywords: C-arm cone-beam CT; fiducial marker; motion artifacts; motion compensation; weight-bearing knee

Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. Part I. Numerical model-based optimization - Choi, Jang-Hwan; Fahrig, Rebecca; Keil, Andreas; Besier, Thor; Pal, Saikat; McWalter, Emily; Beaupre, Gary; Maier, Andreas (Medical Physics, 2013)

Fiducial marker-based motion compensation for the acquisition of 3D knee geometry under weight-bearing conditions using a C-arm CT scanner - Choi, Jang-Hwan; Maier, Andreas; Keil, Andreas; Pal, Saikat; McWalter, Emily; Fahrig, Rebecca (Proc. 54th AAPM Annual Meeting and Technical Exhibits, 2012)

Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery - Fan, Fuxin; Kreher, Björn W.; Keil, Holger; Maier, Andreas; Huang, Yixing (Medical Physics, 2022)
Purpose: Fiducial markers are commonly used in navigation assisted minimally invasive spine surgery and they help transfer image coordinates into real world coordinates. In practice, these markers might be located outside the field-of-view (FOV) of C-arm cone-beam computed tomography (CBCT) systems used in intraoperative surgeries, due to the limited detector sizes. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation. Methods: In this work, we propose two fiducial marker detection methods: direct detection from distorted markers (direct method) and detection after marker recovery (recovery method). For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed. For marker recovery, a task-specific learning strategy is proposed to recover markers from severely truncated data. Afterwards, a conventional marker detection algorithm is applied for position detection. Results: The two methods are evaluated on simulated and real data. The direct method achieves 100% detection rates with maximal 2-pixel difference on simulated data with normal truncation and simulated data with severe noise, but fails to detect all the markers in extremely severe truncation case. The recovery method detects all the markers successfully with maximal 1 pixel difference on all simulated data sets. For real data, both methods achieve 100% marker detection rates with mean registration error below 0.2 mm. Conclusions: Our experiments demonstrate that the direct method is capable of detecting distorted markers accurately and the recovery method with task-specific learning has high robustness and generalizability on various data sets. The task-specific learning is able to reconstruct structures of interest outside the FOV from severely truncated data, which has the potential to empower CBCT systems with new applications.<br
Keywords: minimally invasive spine surgery; marker detection; marker recovery; task-specific learning; truncation correction

Field of Interest Proposal for Augmented Mitotic Cell Count: Comparison of Two Convolutional Networks - Aubreville, Marc; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas (Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, 2019)
Most tumor grading systems for human as for veterinary histopathology are based upon the absolute count of mitotic figures in a certain reference area of a histology slide. Since time for prognostication is limited in a diagnostic setting, the pathologist will oftentimes almost arbitrarily choose a certain field of interest assumed to have the highest mitotic activity. However, as mitotic figures are commonly very sparse on the slide and often have a patchy distribution, this poses a sampling problem which is known to be able to influence the tumor prognostication. On the other hand, automatic detection of mitotic figures can’t yet be considered reliable enough for clinical application. In order to aid the work of the human expert and at the same time reduce variance in tumor grading, it is beneficial to assess the whole slide image (WSI) for the highest mitotic activity and use this as a reference region for human counting. For this task, we compare two methods for region of interest proposal, both based on convolutional neural networks (CNN). For both approaches, the CNN performs a segmentation of the WSI to assess mitotic activity. The first method performs a segmentation of mitotic cells at the original image resolution, while the second approach performs a segmentation operation at a significantly reduced resolution, cutting down on processing complexity. We evaluate the approach using a dataset of 32 completely annotated whole slide images of canine mast cell tumors, where 22 were used for training of the network and 10 for test. Our results indicate that, while the overall correlation to the ground truth mitotic activity is considerably higher (0.936 vs. 0.829) for the approach based upon the fine resolution network, the field of interest choices are only marginally better. Both approaches propose fields of interest that contain a mitotic count in the upper quartile of respective slides.<br
Keywords: Mitotic Figure; Cell Segmentation; Digital Histopathology; Tumor Grading

Field of View Extension in Computed Tomography Using Deep Learning Prior - Huang, Yixing; Gao, Lei; Preuhs, Alexander; Maier, Andreas (Bildverarbeitung für die Medizin: Algorithmen – Systeme – Anwendungen, 2020)
In computed tomography (CT), data truncation is a common problem. Images reconstructed by the standard filtered back-projection algorithm from truncated data suffer from cupping artifacts inside the field-of-view (FOV), while anatomical structures are severely distorted or missing outside the FOV. Deep learning, particularly the U-Net, has been applied to extend the FOV as a post-processing method. Since image-to-image prediction neglects the data fidelity to measured projection data, incorrect structures, even inside the FOV, might be reconstructed by such an approach. Therefore, generating reconstructed images directly from a post-processing neural network is inadequate. In this work, we propose a data consistent reconstruction method, which utilizes deep learning reconstruction as prior for extrapolating truncated projections and a conventional iterative reconstruction to constrain the reconstruction consistent to measured raw data. Its efficacy is demonstrated in our study, achieving small average root-mean-square error of 24 HU inside the FOV and a high structure similarity index of 0.993 for the whole body area on a test patient's CT data.
Keywords: Deep learning; data truncation; FOV extension; data consistency

FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales - Bergler, Christian; Gebhard, Alexander; Towers, Jared R.; Butyrev, Leonid; Sutton, Gary J.; Shaw, Tasli J. H.; Maier, Andreas; Nöth, Elmar (Scientific Reports, 2021)
Biometric identification techniques such as photo-identification require an array of unique natural markings to identify individuals. From 1975 to present, Bigg’s killer whales have been photo-identified along the west coast of North America, resulting in one of the largest and longest-running cetacean photo-identification datasets. However, data maintenance and analysis are extremely time and resource consuming. This study transfers the procedure of killer whale image identification into a fully automated, multi-stage, deep learning framework, entitled FIN-PRINT. It is composed of multiple sequentially ordered sub-components. FIN-PRINT is trained and evaluated on a dataset collected over an 8-year period (2011–2018) in the coastal waters off western North America, including 121,000 human-annotated identification images of Bigg’s killer whales. At first, object detection is performed to identify unique killer whale markings, resulting in 94.4% recall, 94.1% precision, and 93.4% mean-average-precision (mAP). Second, all previously identified natural killer whale markings are extracted. The third step introduces a data enhancement mechanism by filtering between valid and invalid markings from previous processing levels, achieving 92.8% recall, 97.5%, precision, and 95.2% accuracy. The fourth and final step involves multi-class individual recognition. When evaluated on the network test set, it achieved an accuracy of 92.5% with 97.2% top-3 unweighted accuracy (TUA) for the 100 most commonly photo-identified killer whales. Additionally, the method achieved an accuracy of 84.5% and a TUA of 92.9% when applied to the entire 2018 image collection of the 100 most common killer whales. The source code of FIN-PRINT can be adapted to other species and will be publicly available.<br

First Approaches Towards Automatic Detection of Microaneurysms in OCTA Images - Husvogt, Lennart; Alibhai, A. Yasin; Moult, Eric M.; Fujimoto, James G.; Waheed, Nadia K.; Maier, Andreas (Bildverarbeitung für die Medizin 2018, 2018)
We investigated automatic detection of micro aneurysms in optical coherence tomo-graphy angiography. Data of two patients was gathered at the New England Eye Center. Patients with diabetic retinopathy were imaged on an Optovue Avanti device.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.firsta
Keywords: OCT; angiography; microaneurysms; automatic detection; random forest

First in-vivo Experiments with Large field-of-view Flat Panel Photon-Counting Detector - Moiz, Ahmad; Fahrig, Rebecca; Spahn, Martin; Choi, Jang-Hwan; Köster, Niko; Reitz, Silke; Hinshaw, Waldo; Pung, Leland; Moore, Teri; Maier, Andreas; Müller, Kerstin (Proceedings of the fourth international conference on image formation in x-ray computed tomography, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.firsti

First Steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain - Kunzmann, Sonja; Marzahl, Christian; Denzinger, Felix; Bertram, Christof; Klopfleisch, Robert; Breininger, Katharina; Christlein, Vincent; Maier, Andreas (Informatik aktuell, 2022)
Annotating data, especially in the medical domain, requires expert knowledge and a lot of effort. This limits the amount and/or usefulness of available medical data sets for experimentation. Therefore, developing strategies to increase the number of annotations while lowering the needed domain knowledge is of interest. A possible strategy is the use of gamification, i.e. transforming the annotation task into a game. We propose an approach to gamify the task of annotating lung fluid cells from pathological whole slide images (WSIs). As the domain is unknown to non-expert annotators, we transform images of cells to the domain of flower images using a CycleGAN architecture. In this more assessable domain, non-expert annotators can be (t)asked to annotate different kinds of flowers in a playful setting. In order to provide a proof of concept, this work shows that the domain transfer is possible by evaluating an image classification network trained on real cell images and tested on the cell images generated by the CycleGAN network (reconstructed cell images) as well as real cell images. The classification network reaches an average accuracy of 94.73% on the original lung fluid cells and 95.25% on the transformed lung fluid cells, respectively. Our study lays the foundation for future research on gamification using CycleGANs.<br
CRIS-Team Scopus Importer:2022-05-06

Flexible Log File Parsing Using Hidden Markov Models - Kuhnert, Nadine; Maier, Andreas (2019)
We aim to model unknown file processing. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text by focusing only on those frequent patterns which lead to the desired output table similar to Vaarandi [10]. Second, we transform the found frequent patterns and the output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific, however, flexible representation of a pattern for log file processing. With changes in the raw log file distorting learned patterns, we aim the model to adapt automatically in order to maintain high quality output. After training our model on one system type, applying the model and the resulting parsing rule to a different system with slightly different log file patterns, we achieve an accuracy over 99%.<br
Keywords: Hidden Markov Models; Parameter Extraction; Parsing; Text Mining; Information Retrieval

Flexible Unfolding of Circular Structures for Rendering Textbook-Style Cerebrovascular Maps - Rist, Leonhard; Taubmann, Oliver; Ditt, Hendrik; Sühling, Michael; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, 2023)
Comprehensive, contiguous visualizations of the main cerebral arteries and the surrounding parenchyma offer considerable potential for improving diagnostic workflows in cerebrovascular disease, e.g., for fast assessment of vascular topology and lumen in stroke patients. Unfolding the brain vasculature into a 2D overview is, however, infeasible using common Curved Planar Reformation (CPR) due to the circular structure of the Circle of Willis (CoW) and the spatial configuration of the vessels typically rendering them unsuitable for mapping onto simple geometric primitives. We propose CeVasMap, extending the As-Rigid-As-Possible (ARAP) deformation by a smart initialization of the required mesh to map the CoW as well as a merging of neighboring vessels depending on the resulting degree of distortion. Otherwise, vessels are unfolded and attached individually, creating a textbook-style overview image. We provide an extensive distortion analysis, comparing the vector fields of individual and merged unfoldings of each vessel to their CPR results. In addition to enabling unfolding of circular structures, our method is on par in terms of incurred distortions to optimally oriented CPRs for individual vessels and comparable to unfavorable CPR orientations when merging the complete CoW with a median distortion of 65 μm/m
Keywords: Vessel unfolding; Distortion; Cerebrovascular disease

FlexParser—The adaptive log file parser for continuous results in a changing world - Kuhnert, Nadine; Maier, Andreas (Journal of Software: Evolution and Process, 2022)
Any modern system writes events into files, called log files. Those contain crucial information which are subject to various analyses. Examples range from cybersecurity, intrusion detection over usage analyses to trouble shooting. Before data analysis is possible, desired information needs to be extracted first out of the semi-structured log messages. State-of-the-art event parsing often assumes static log events. However, any modern system is updated consistently and with updates also log file structures can change. We call those changes “mutation” and study parsing performance for different mutation cases. Latest research discovers mutations using anomaly detection post mortem, however, does not cover actual continuous parsing. Thus, we propose a novel and flexible parser, called FlexParser, which can extract desired values despite gradual changes in the log messages. It implies basic text preprocessing followed by a supervised Deep Learning method. We train a stateful LSTM on parsing one event per data set. Statefulness enforces the model to learn log message structures across several examples. Our model was tested on seven different, publicly available log file data sets and various kinds of mutations. Exhibiting an average F1-Score of 0.98, it outperforms other Deep Learning methods as well as state-of-the-art unsupervised parsers<br
Keywords: deep learning; flexible parsing; log parser; LSTM; system log

Flow Void Analysis: Multiscale Quantitation and Visualization of Choriocapillaris Alterations in Age-Related Macular Degeneration Using OCT Angiography - Alibhai, A. Yasin; Moult, Eric M.; Rebhun, Carl B.; Moreira-Neto, Carlos; Novais, Eduardo A.; Lee, Byungkun; Schottenhamml, Julia; Husvogt, Lennart; Maier, Andreas; Rosenfeld, Philip; Duker, Jay S.; Waheed, Nadia K.; et al. (Investigative Ophthalmology & Visual Science, 2017)
Purpose : The choriocapillaris (CC) is important in the pathophysiology of age-related macular degeneration (AMD). This study aims to develop optical coherence tomography angiography (OCTA) techniques for quantifying and visualizing CC alterations. Methods : We developed a novel metric for quantifying CC flow voids (areas of no/low blood flow), as well as a display method: multiscale flow void mapping (MFVM). The metric was tested on normal controls (13 eyes, 9 subjects; 68.2±6.89 y/o), eyes with small drusen (volume<0.02 mm3; 8 eyes, 7 subjects; 69.3±11.22 y/o), large drusen (volume≥0.02 mm3; 6 eyes, 5 subjects; 80.0±5.37 y/o), nascent geographic atrophy (nGA: lesions showing hypertransmission over a linear dimension >125µm but not evident on routine fundus imaging) and/or drusen-associated geographic atrophy (DAGA) (10 eyes, 9 patients; 74.0±7.39 y/o), and geographic atrophy (GA) (6 eyes, 3 subjects; 81.7±9.40 y/o). Results : There was a trend of increasing CC alteration with age, though this was only significant (p<0.05) in controls (Fig 1A). There was also a trend of increasing CC alteration with disease state (Fig 1C): Mann-Whitney U testing (without Bonferroni correction) showed significantly (p<0.05) different metric values between nGA/DAGA and all other groups, and GA and all other groups (Fig 1D); with Bonferroni correction nGA/DAGA could not be distinguished from large drusen. This analysis did not account for inter-group age variations (Fig 1B), or multiple eyes from the same patient. MFVM was useful for visualizing CC alterations (Fig 2). Conclusions : Quantitative analysis and MVFM may be useful for monitoring CC alteration as a marker of disease progressio
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.flowvo
Keywords: oct; octa; oct angiography; amd; age-related macular degeneration

Font Augmentation: Implant and Surgical Tool Simulation for X-Ray Image Processing - Kordon, Florian Johannes; Maier, Andreas; Swartman, Benedict; Kunze, Holger (Bildverarbeitung für die Medizin 2020, 2020)
This study investigates a novel data augmentation approach for simulating surgical instruments, tools, and implants by image composition with transformed characters, numerals, and abstract symbols from open-source fonts. We analyse its suitability for the common spatial learning tasks of multi-label segmentation and anatomical landmark detection. The proposed technique is evaluated on 38 clinical intraoperative X-ray images with a high occurrence of objects overlaying the target anatomy. We demonstrate increased robustness towards superimposed surgical objects by incorporating our technique and provide an empirical rationale about the neglectable influence of realistic object shape and intensity information.
Keywords: X-Ray; Intra-operative Imaging; Data Augmentation; Bone Segmentation; Landmark Localization

ForestNet – Automatic Design of Sparse Multilayer Perceptron Network Architectures Using Ensembles of Randomized Trees - Rodriguez Salas, Dalia; Ravikumar, Nishant; Seuret, Mathias; Maier, Andreas (Pattern Recognition, 2020)
In this paper, we introduce a mechanism for designing the architecture of a Sparse Multi-Layer Perceptron network, for classification, called ForestNet. Networks built using our approach are capable of handling high-dimensional data and learning representations of both visual and non-visual data. The proposed approach first builds an ensemble of randomized trees in order to gather information on the hierarchy of features and their separability among the classes. Subsequently, such information is used to design the architecture of a sparse network, for a specific data set and application. The number of neurons is automatically adapted to the dataset. The proposed approach was evaluated using two non-visual and two visual datasets. For each dataset, 4 ensembles of randomized trees with different sizes were built. In turn, per ensemble, a sparse network architecture was designed using our approach and a fully connected network with same architecture was also constructed. The sparse networks defined using our approach consistently outperformed their respective tree ensembles, achieving statistically significant improvements in classification accuracy. While we do not beat state-of-art results with our network size and the lack of data augmentation techniques, our method exhibits very promising results, as the sparse networks performed similarly to their fully connected counterparts with a reduction of more than 98% of connections in the visual tasks.<br
Keywords: Multilayer Perceptron; Random Forest; Randomized Trees; Sparse Neural Networks; Network Architecture

Form Follows Function: Smart Network Design Enables Zero-shot Network Reuse - Fu, Weilin; Husvogt, Lennart; Breininger, Katharina; Schaffert, Roman; Abu-Qamar, Omar; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2022)
In this work, we construct and train a learning-based neural network pipeline for retinal vessel segmentation on fundus images and transfer the preprocessing module directly onto OCTA en face projections. Without additional fine-tuning, the transferred module retains the edge-preserving denoising functionality, as our smart network design enables zero-shot domain adaptation. Compared to a denoising network trained on OCTA data, the transferred preprocessing module is superior with regard to performance, generalization ability, and numerical stability. In addition, Frangi’s vessel segmentation of the preprocessed images outperforms the predictions using pretrained full segmentation networks. The selection of fitting Gaussian scales of the Frangi filter in the target domain can bypass the over/under-segmentation problem caused by the vessel diameter mismatch between the two domains. The contribution of this work is two-fold: we provide an exemplary evidence that smart design of network pipelines allows for flexible functional module reuse across different imaging modalities, i.e. zero-shot domain adaptation; and we discover a deep learning-based vessel segmentation pipeline for OCTA en face images which reaches an AUC score of 0.9418 without additional training.<br
CRIS-Team Scopus Importer:2022-05-06

Fourier-based Reduction of Directed Streak Arifacts in Cone-Beam CT - Gawellek, Julia; Bier, Bastian; Gold, Garry; Maier, Andreas (Bildverarbeitung für die Medizin 2018, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.fourie

Fourier Consistency-Based Motion Estimation in Rotational Angiography - Unberath, Mathias; Berger, Martin; Aichert, André; Maier, Andreas (Proc. Bildverarbeitung für die Medizin (BVM) 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fourie

Frangi-Net: A Neural Network Approach to Vessel Segmentation - Fu, Weilin; Breininger, Katharina; Schaffert, Roman; Ravikumar, Nishant; Würfl, Tobias; Fujimoto, James G.; Moult, Eric M.; Maier, Andreas (BildVerarbeitung für die Medizin (BVM) 2018, 2018)
In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network (“Frangi-Net”), and illustrate that the Frangi-Net is equivalent to the original Frangi filter. Furthermore, we show that, as a neural network, Frangi-Net is trainable. We evaluate the proposed method on a set of 45 high resolution fundus images. After fine-tuning, we observe both qualitative and quantitative improvements in the segmentation quality compared to the original Frangi measure, with an increase up to 17% in F1 scor
Keywords: Vessel segmentation; Precision learning; Frangi-Net; Fundus imaging

Free-breathing fat and R2∗ quantification in the liver using a stack-of-stars multi-echo acquisition with respiratory-resolved model-based reconstruction - Schneider, Manuel; Benkert, Thomas; Solomon, Eddy; Nickel, Dominik; Fenchel, Matthias; Kiefer, Berthold; Maier, Andreas; Chandarana, Hersh; Block, Kai Tobias (Magnetic Resonance in Medicine, 2020)
Purpose: To develop a free-breathing hepatic fat and (Formula presented.) quantification method by extending a previously described stack-of-stars model-based fat-water separation technique with additional modeling of the transverse relaxation rate (Formula presented.) . Methods: The proposed technique combines motion-robust radial sampling using a stack-of-stars bipolar multi-echo 3D GRE acquisition with iterative model-based fat-water separation. Parallel-Imaging and Compressed-Sensing principles are incorporated through modeling of the coil-sensitivity profiles and enforcement of total-variation (TV) sparsity on estimated water, fat, and (Formula presented.) parameter maps. Water and fat signals are used to estimate the confounder-corrected proton-density fat fraction (PDFF). Two strategies for handling respiratory motion are described: motion-averaged and motion-resolved reconstruction. Both techniques were evaluated in patients (n = 14) undergoing a hepatobiliary research protocol at 3T. PDFF and (Formula presented.) parameter maps were compared to a breath-holding Cartesian reference approach. Results: Linear regression analyses demonstrated strong (r > 0.96) and significant (P ≪.01) correlations between radial and Cartesian PDFF measurements for both the motion-averaged reconstruction (slope: 0.90; intercept: 0.07%) and the motion-resolved reconstruction (slope: 0.90; intercept: 0.11%). The motion-averaged technique overestimated hepatic (Formula presented.) values (slope: 0.35; intercept: 30.2 1/s) compared to the Cartesian reference. However, performing a respiratory-resolved reconstruction led to better (Formula presented.) value consistency (slope: 0.77; intercept: 7.5 1/s). Conclusions: The proposed techniques are promising alternatives to conventional Cartesian imaging for fat and (Formula presented.) quantification in patients with limited breath-holding capabilities. For accurate (Formula presented.) estimation, respiratory-resolved reconstruction should be used.
CRIS-Team Scopus Importer:2020-04-28
Keywords: compressed sensing; free-breathing fat/ R2∗ quantification; multi-echo 3D stack-of-stars GRE; nonalcoholic fatty liver disease; radial sampling; respiratory motion-resolved reconstruction

Free-Breathing, Self-Navigated and Dynamic 3-D Multi-Contrast Cardiac CINE Imaging Using Cartesian Sampling and Compressed Sensing - Hoppe, Elisabeth; Wetzl, Jens; Forman, Christoph; Koerzdoerfer, Gregor; Schneider, Manuel; Speier, Peter; Schmidt, Michaela; Maier, Andreas (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (27th Annual Meeting & Exhibition), 2019)
We present a free-breathing multi-contrast 3-D cardiac CINE acquisition and reconstruction technique based on Compressed Sensing. Inversion pulses were repeatedly applied during a continuous acquisition to sample contrast- and cardiac-resolved 3-D data, while a self-navigation method was applied for respiratory gating. Validation was performed in a phantom, showing recovery curves and T1* maps in good correlation to known T1 values for the phantom as well as a MOLLI reference measurement. Feasibility for in-vivo application was demonstrated in a healthy voluntee
Keywords: Magnetic Resonance Imaging; Cardiac CINE Magnetic Resonance Imaging; Multicontrast Imaging; Compressed Sensing

Free-Breathing Self-Navigated Isotropic 3-D CINE Imaging of the Whole Heart using Adaptive Triggering and Retrospective Gating - Wetzl, Jens; Lugauer, Felix; Kroeker, Randall; Schmidt, Michaela; Maier, Andreas; Forman, Christoph (Proceedings of the 25th Annual Meeting of the ISMRM, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.freebr_8

Free-Breathing, Self-Navigated Isotropic 3-D CINE Imaging of the Whole Heart Using Cartesian Sampling - Wetzl, Jens; Lugauer, Felix; Schmidt, Michaela; Maier, Andreas; Hornegger, Joachim; Forman, Christoph (Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.freebr

Free-Breathing Whole-Heart Coronary MRA: Motion Compensation Integrated into 3D Cartesian Compressed Sensing Reconstruction - Forman, Christoph; Grimm, Robert; Hutter, Jana; Maier, Andreas; Hornegger, Joachim; Zenge, Michael O. (MICCAI 2013, Part II, LNCS 8150, 2013)

Frist experimental results on long-object imaging using a reverse helical trajectory with a C-arm system - Yu, Zhicong; Maier, Andreas; Schönborn, Manfred; Vogt, Florian; Köhler, Christoph; Lauritsch, Günter; Hornegger, Joachim; Noo, Frédéric (Proceedings of The second international conference on image formation in x-ray computed tomography, 2012)

Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks - Vesal, Sulaiman; Maier, Andreas; Ravikumar, Nishant (Journal of Imaging, 2020)
Cardiac magnetic resonance (CMR) imaging is used widely for morphological assessment and diagnosis of various cardiovascular diseases. Deep learning approaches based on 3D fully convolutional networks (FCNs), have improved state-of-the-art segmentation performance in CMR images. However, previous methods have employed several pre-processing steps and have focused primarily on segmenting low-resolutions images. A crucial step in any automatic segmentation approach is to first localize the cardiac structure of interest within the MRI volume, to reduce false positives and computational complexity. In this paper, we propose two strategies for localizing and segmenting the heart ventricles and myocardium, termed multi-stage and end-to-end, using a 3D convolutional neural network. Our method consists of an encoder–decoder network that is first trained to predict a coarse localized density map of the target structure at a low resolution. Subsequently, a second similar network employs this coarse density map to crop the image at a higher resolution, and consequently, segment the target structure. For the latter, the same two-stage architecture is trained end-to-end. The 3D U-Net with some architectural changes (referred to as 3D DR-UNet) was used as the base architecture in this framework for both the multi-stage and end-to-end strategies. Moreover, we investigate whether the incorporation of coarse features improves the segmentation. We evaluate the two proposed segmentation strategies on two cardiac MRI datasets, namely, the Automatic Cardiac Segmentation Challenge (ACDC) STACOM 2017, and Left Atrium Segmentation Challenge (LASC) STACOM 2018. Extensive experiments and comparisons with other state-of-the-art methods indicate that the proposed multi-stage framework consistently outperforms the rest in terms of several segmentation metrics. The experimental results highlight the robustness of the proposed approach, and its ability to generate accurate high-resolution segmentations, despite the presence of varying degrees of pathology-induced changes to cardiac morphology and image appearance, low contrast, and noise in the CMR volumes.<br
Keywords: cardiac MRI; deep neural network; CNN; multistage segmentation; MRI segmentation; cardiovascular diseases

Fully automated AI-based cardiac motion parameter extraction – application to mitral and tricuspid valves on long-axis cine MR images - Yoon, Seung Su; Fischer, Carola; Amsel, Daniel; Monzon, Maria; Toupin, Solenn; Pezel, Théo; Garot, Jérôme; Wetzl, Jens; Maier, Andreas; Giese, Daniel; Giese, Daniel (European Journal of Radiology, 2023)
Purpose: In cardiac MRI, valve motion parameters can be useful for the diagnosis of cardiac dysfunction. In this study, a fully automated AI-based valve tracking system was developed and evaluated on 2- or 4-chamber view cine series on a large cardiac MR dataset. Automatically derived motion parameters include atrioventricular plane displacement (AVPD), velocities (AVPV), mitral or tricuspid annular plane systolic excursion (MAPSE, TAPSE), or longitudinal shortening (LS). Method: Two sequential neural networks with an intermediate processing step are applied to localize the target and track the landmarks throughout the cardiac cycle. Initially, a localisation network is used to perform heatmap regression of the target landmarks, such as mitral, tricuspid valve annulus as well as apex points. Then, a registration network is applied to track these landmarks using deformation fields. Based on these outputs, motion parameters were derived. Results: The accuracy of the system resulted in deviations of 1.44 ± 1.32 mm, 1.51 ± 1.46 cm/s, 2.21 ± 1.81 mm, 2.40 ± 1.97 mm, 2.50 ± 2.06 mm for AVPD, AVPV, MAPSE, TAPSE and LS, respectively. Application on a large patient database (N = 5289) revealed a mean MAPSE and LS of 9.5 ± 3.0 mm and 15.9 ± 3.9 % on 2-chamber and 4-chamber views, respectively. A mean TAPSE and LS of 13.4 ± 4.7 mm and 21.4 ± 6.9 % was measured. Conclusion: The results demonstrate the versatility of the proposed system for automatic extraction of various valve-related motion parameters.
CRIS-Team Scopus Importer:2023-08-11
Keywords: AI; CMR; Longitudinal shortening; Mitral valve motion; Tricuspid valve motion

Fully Automated Data-Driven Respiratory Signal Extraction From SPECT Images Using Laplacian Eigenmaps - Sanders, James Chester; Ritt, Philipp; Kuwert, Torsten; Vija, A. Hans; Maier, Andreas (IEEE Transactions on Medical Imaging, 2016)
We propose a data-driven method for extracting a respiratory surrogate signal from SPECT list-mode data. The approach is based on dimensionality reduction with Laplacian Eigenmaps. By setting a scale parameter adaptively and adding a series of post-processing steps to correct polarity and normalization between projections, we enable fully-automatic operation and deliver a respiratory surrogate signal for the entire SPECT acquisition. We validated the method using 67 patient scans from three acquisition types (myocardial perfusion, liver shunt diagnostic, lung inhalation/perfusion) and an Anzai pressure belt as a gold standard. The proposed method achieved a mean correlation against the Anzai of 0.81 ± 0.17 (median 0.89). In a subsequent analysis, we characterize the performance of the method with respect to count rates and describe a predictor for identifying scans with insufficient statistics. To the best of our knowledge, this is the first large validation of a data-driven respiratory signal extraction method published thus far for SPECT, and our results compare well with those reported in the literature for such techniques applied to other modalities such as MR and PET.
EVALuna2:13863

Fully automated Machine Learning-based selection of optimal bSSFP frequency offset for artifact reduction in cardiac MRI - Yoon, Seung Su; Gadjimuradov, Fasil; Schmidt, Michaela; Wetzl, Jens; Maier, Andreas (Artificial Intelligence in Cardiovascular Magnetic Resonance Imaging - A Joint Summit of the EACVI and SCMR, 2022)

Fully automatic AI-based extraction of mitral valve motion parameters on long axis CINE images – validation and application on N=2000 patient datasets - Yoon, Seung Su; Fischer, Carola; Toupin, Solenn; Pezel, Théo; Garot, Jérome; Wetzl, Jens; Maier, Andreas; Giese, Daniel (Society for Cardiovascular Magnetic Resonance 25th Annual Scientific Sessions, 2022)

Fully automatic AI-based valve motion parameter extraction on long axis CINE images – application on N=11000 patient datasets - Yoon, Seung Su; Fischer, Carola; Toupin, Solenn; Pezel, Théo; Garot, Jérome; Wetzl, Jens; Maier, Andreas; Giese, Daniel (Artificial Intelligence in Cardiovascular Magnetic Resonance Imaging - A Joint Summit of the EACVI and SCMR, 2022)

Fully Automatic Assessment of Speech of Children with Cleft Lip and Palate - Maier, Andreas; Nöth, Elmar; Batliner, Anton; Nkenke, Emeka; Schuster, Maria (Informatica, 2006)

Fully-automatic CT data preparation for interventional X-ray skin dose simulation - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Stimpel, Bernhard; Syben, Christopher; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Informatik Aktuell Bildverarbeitung für die Medizin 2020 Algorithmen - Systeme - Anwendungen, 2020)
Recently, deep learning (DL) found its way to interventional X-ray skin dose estimation. While its performance was found to be acceptable, even more accurate results could be achieved if more data sets were available for training. One possibility is to turn to computed tomography (CT) data sets. Typically, computed tomography (CT) scans can be mapped to tissue labels and mass densities to obtain training data. However, care has to be taken to make sure that the different clinical settings are properly accounted for. First, the interventional environment is characterized by wide variety of table setups that are significantly different from the typical patient tables used in conventional CT. This cannot be ignored, since tables play a crucial role in sound skin dose estimation in an interventional setup, e.,g., when the X-ray source is directly underneath a patient (posterior-anterior view). Second, due to interpolation errors, most CT scans do not facilitate a clean segmentation of the skin border. As a solution to these problems, we applied connected component labeling (CCL) and Canny edge detection to (a) robustly separate the patient from the table and (b) to identify the outermost skin layer. Our results show that these extensions enable fully-automatic, generalized pre-processing of CT scans for further simulation of both skin dose and corresponding X-ray projection
Keywords: X-ray simulation; Computed tomography; Data preparation

Fully automatic extraction of mitral valve annulus motion parameters on long axis CINE CMR using deep learning - Monzon, Maria; Yoon, Seung Su; Fischer, Carola; Maier, Andreas; Wetzl, Jens; Giese, Daniel (International Society for Magnetic Resonance in Medicine (ISMRM) 29th Annual Meeting & Exhibition, 2021)

Fully Automatic Head Motion Correction for Interventional C-arm Systems using Fiducial Markers - Müller, Kerstin; Berger, Martin; Choi, Jang-Hwan; Datta, Sanjit; Gehrisch, Sonja; Moore, Teri; Marks, Michael P; Maier, Andreas; Fahrig, Rebecca (Proceedings of the 13th Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.fullya_7

Fully Automatic Scar Segmentation on LGE Images with Reduced Contrast Agent Dose - Vornehm, Marc; Spiske, Ute; Fenski, Maximilian; Wetzl, Jens; Preuhs, Elisabeth; Maier, Andreas; Schulz-Menger, Jeanette; Giese, Daniel (Proceedings of the SCMR 26th Annual Scientific Sessions, 2023)

Fully automatic segmentation of left ventricular anatomy in 3-DLGE-MRI - Kurzendorfer, Tanja; Forman, Christoph; Schmidt, Michaela; Tillmanns, Christoph; Maier, Andreas; Brost, Alexander (Computerized Medical Imaging and Graphics, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.fullya_0

Fully Automatic Segmentation of Papillary Muscles in 3-D LGE-MRI - Kurzendorfer, Tanja; Brost, Alexander; Forman, Christoph; Schmidt, Michaela; Tillmanns, Christoph; Maier, Andreas (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.fullya_7

Fully automatic separation of three capillary plexuses in the macular region - Schottenhamml, Julia; Ploner, Stefan; Moult, Eric; Duker, Jay S.; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2020)
CRIS-Team WoS Importer:2020-09-11

Fusing Motion Estimates for CBCT Reconstruction - Bier, Bastian; Unberath, Mathias; Geimer, Tobias; Ravikumar, Nishant; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.fusing

GAN-based Augmentation of Mammograms to Improve Breast Lesion Detection - El-Ghoussani, Amir; Rodriguez Salas, Dalia; Seuret, Mathias; Maier, Andreas (Bildverarbeitung für die Medizin 2022, 2022)
Mammography is an important part of breast cancer diagnostics, as it allows to inspect the inner breast structure without physically penetrating breast tissue. Commonly, mammograms tend to vary in their visual appearance based on the specific device and the circumstances under which the mammogram is acquired. Such images could cause artificial intelligence algorithms to fail as they can introduce an undesired variation into the data. This study intends to put these images to use by utilizing a cycle-consistent Generative Adversarial Network (GAN) in order to augment the training data by diversifying instances of each visual domain into all the available ones. A publicly available dataset was augmented to train a detection network; the GANs used for the augmentation were trained with an in-house dataset with three visually different domains.Results show that using our augmentation technique consistently increases the detection performance by reaching a mean average precision of up to 0.82 against 0.77 without augmenting the data.<br

Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems - Packhäuser, Kai; Folle, Lukas; Thamm, Florian; Maier, Andreas (2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023)

Generation of Personalized Computational Phantoms Using Only Patient Metadata - Zhong, Xia; Strobel, Norbert; Sanders, James Chester; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.genera_4

Generation of Synthetic 3D Data Using Simulated MR Examinations in Augmented Reality - Serra Juhé, Aniol; Rinck, Daniel; Maier, Andreas (Proceedings - International Symposium on Biomedical Imaging, 2023)
In the future, medical imaging devices such as computed tomography (CT) and magnetic resonance (MR) are expected to become increasingly autonomous. Therefore, design criteria and workflow of such devices will change substantially. Moreover, sensor data of the system are required to develop scene understanding algorithms to support and guide the user. In this work, we present an approach to generate synthetic 3D point cloud data from a simulated MR examination experienced on the Microsoft Hololens2. The complete workflow of an MR examination using a virtual, autonomous MR scanner is reproduced in an AR scene. The user is recorded by a system of active stereo vision RGBD-cameras while interacting with AR elements. A registration routine of the AR scene and the RGBD-cameras is described, and accuracy measurements are provided. The real point clouds are fused with virtually generated point clouds from the AR scene.
CRIS-Team Scopus Importer:2023-10-06
Keywords: Augmented Reality; Synthetic data; Workflow Analysis

Generation of synthetic Image Data for the Evaluation of Brain Shift Compensation Methods - Bayer, Siming; Maier, Andreas; Fahrig, Rebecca; Ostermeier, Martin (3rd Conference on Image-Guided Interventions & Fokus Neuroradiologie, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.genera_1