Automated 3D Defect Detection based on Simulated Reference - Sukowski, Frank; Suth, Daniel; Waldyra, Adrian; Grulich, Tobias; Jung, Alexander (2023)

Automated artifact detection in abbreviated dynamic contrast-enhanced (DCE) MRI-derived maximum intensity projections (MIPs) of the breast - Kapsner, Lorenz; Ohlmeyer, Sabine; Folle, Lukas; Laun, Frederik Bernd; Nagel, Armin Michael; Liebert, Andrzej; Schreiter, Hannes; Beckmann, Matthias; Uder, Michael; Wenkel, Evelyn; Bickelhaupt, Sebastian (European Radiology, 2022)
Objectives To automatically detect MRI artifacts on dynamic contrast-enhanced (DCE) maximum intensity projections (MIPs) of the breast using deep learning. Methods Women who underwent clinically indicated breast MRI between October 2015 and December 2019 were included in this IRB-approved retrospective study. We employed two convolutional neural network architectures (ResNet and DenseNet) to detect the presence of artifacts on DCE MIPs of the left and right breasts. Networks were trained on images acquired up to and including the year 2018 using a 5-fold cross-validation (CV). Ensemble classifiers were built with the resulting CV models and applied to an independent holdout test dataset, which was formed by images acquired in 2019. Results Our study sample contained 2265 examinations from 1794 patients (median age at first acquisition: 50 years [IQR: 17 years]), corresponding to 1827 examinations of 1378 individuals in the training dataset and 438 examinations of 416 individuals in the holdout test dataset with a prevalence of image-level artifacts of 53% (1951/3654 images) and 43% (381/876 images), respectively. On the holdout test dataset, the ResNet and DenseNet ensembles demonstrated an area under the ROC curve of 0.92 and 0.94, respectively. Conclusion Neural networks are able to reliably detect artifacts that may impede the diagnostic assessment of MIPs derived from DCE subtraction series in breast MRI. Future studies need to further explore the potential of such neural networks to complement quality assurance and improve the application of DCE MIPs in a clinical setting, such as abbreviated protocol
Keywords: Artifacts; Magnetic resonance imaging; Breast; Contrast agent; Neural network

Automated Billing Code Prediction from MRI Log Data - Denck, Jonas; Landschuetz, Wilfried; Nairz, Knud; Heverhagen, Johannes T.; Maier, Andreas; Rothgang, Eva (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (26th Annual Meeting & Exhibition), 2018)

Automated Billing Code Retrieval from MRI Scanner Log Data - Denck, Jonas; Landschütz, Wilfried; Nairz, Knud; Heverhagen, Johannes T.; Maier, Andreas; Rothgang, Eva (Journal of Digital Imaging, 2019)
Although the level of digitalization and automation steadily increases in radiology, billing coding for magnetic resonance imaging (MRI) exams in the radiology department is still based on manual input from the technologist. After the exam completion, the technologist enters the corresponding exam codes that are associated with billing codes in the radiology information system. Moreover, additional billing codes are added or removed, depending on the performed procedure. This workflow is time-consuming and we showed that billing codes reported by the technologists contain errors. The coding workflow can benefit from an automated system, and thus a prediction model for automated assignment of billing codes for MRI exams based on MRI log data is developed in this work. To the best of our knowledge, it is the first attempt to focus on the prediction of billing codes from modality log data. MRI log data provide a variety of information, including the set of executed MR sequences, MR scanner table movements, and given a contrast medium. MR sequence names are standardized using a heuristic approach and incorporated into the features for the prediction. The prediction model is trained on 9754 MRI exams and tested on 1 month of log data (423 MRI exams) from two MRI scanners of the radiology site for the Swiss medical tariffication system Tarmed. The developed model, an ensemble of classifier chains with multilayer perceptron as a base classifier, predicts medical billing codes for MRI exams with a micro-averaged F1-score of 97.8% (recall 98.1%, precision 97.5%). Manual coding reaches a micro-averaged F1-score of 98.1% (recall 97.4%, precision 98.8%). Thus, the performance of automated coding is close to human performance. Integrated into the clinical environment, this work has the potential to free the technologist from a non-value adding an administrative task, therefore enhance the MRI workflow, and prevent coding errors.
CRIS-Team Scopus Importer:2019-07-09
Keywords: Machine learning; Magnetic resonance imaging; Medical coding; Reimbursement; Workflow enhancement

Automated Bone Density Measurements using Deep Learning - Folle, Lukas; Meinderink, Timo; Simon, David; Liphardt, Anna-Maria; Thies, Mareike; Krönke, Gerhard; Schett, Georg; Kleyer, Arnd; Maier, Andreas (23, 2022)
Introduction Hand bone loss is characteristic of rheumatoid or psoriatic arthritis, which clinically manifests with pain, swelling, and stiffness of the affected joints. Typically, hand joints are affected already in the early stages of arthritis1. High-resolution peripheral quantitative computed tomography (HR-pQCT) allows to visualize structural changes in the bone in 3D and enables measurements derived from the scans of the patient’s hand such as the volume of a bone. For this, a segmentation of the scans is necessary, which is reader-dependent and time-consuming, especially in challenging cases2. This work aims to replace this manual expert segmentation with a neural network (figure 1). Materials and methods The segmentation network was trained using 541 HR-pQCT patient scans (RA, PsA, and OA patients with motion grades up to 3) as input and the corresponding expert segmentation of the second metacarpal bone as reference. To optimize the parameters of the network, the current prediction was compared with the expert segmentation using the Dice coefficient. Pre-processing steps included resizing, flipping of left hands, and intensity normalization. The architecture of the segmentation network is based on the 2D U-Net, which was pre-trained on ImageNet. The predictions were resized to the original resolution and converted to the scanner-specific data format as post-processing steps. The scanner then calculated the average bone-mineral density (D100) based on the scan and the corresponding network-generated segmentation. The scans were split into training, validation, and testing with ratios of 70%, 20%, and 10% respectively. Results and discussion The bone segmentation using the neural network on the test set reached a Dice coefficient of 97.2%. A quantitative comparison of the network prediction (blue) and the expert annotation (red) is presented in figure 2. The deviation of the expert annotation from the bone contour is likely due to the shift of the scan when exceeding the specified maximum number of slices of the scanner. Since the neural network operates on a slice level, such deviations are implicitly accounted for. The results of the vBMD analysis are summarized in figure 3. Significant Pearson correlation with 0.999 (p<.001) was achieved for the D100 on the test set. Still, a slight offset is present in the predictions, which might lead to underestimating the D100. However, this could be eliminated in a calibration step beforehand. Conclusion Neural networks can be used to generate accurate segmentations of the MCP2 head even with erosions present and can be integrated into the clinical workflow with a performance on par with expert annotators and save time through the automation of the annotation process. <
Keywords: deep learning; bone imaging; segmentation

Automated Calculation of Alberta Stroke Program Early CT Score Validation in Patients With Large Hemispheric Infarct - Albers, Gregory W.; Wald, Michael J.; Mlynash, Michael; Endres, Juergen; Bammer, Roland; Straka, Matus; Maier, Andreas; Hinson, Holly E.; Sheth, Kevin N.; Kimberly, W. Taylor; Molyneaux, Bradley J. (Stroke, 2019)
Background and Purpose- We compared the Alberta Stroke Program Early CT Score (ASPECTS), calculated using a machine learning-based automatic software tool, RAPID ASPECTS, as well as the median score from 4 experienced readers, with the diffusion-weighted imaging (DWI) ASPECTS obtained following the baseline computed tomography (CT) in patients with large hemispheric infarcts. Methods- CT and magnetic resonance imaging scans from the GAMES-RP study, which enrolled patients with large hemispheric infarctions (82-300 mL) documented on DWI-magnetic resonance imaging, were evaluated by blinded experienced readers to determine both CT and DWI ASPECTS. The CT scans were also evaluated by an automated software program (RAPID ASPECTS). Using the DWI ASPECTS as a reference standard, the median CT ASPECTS of the clinicians and the automated score were compared using the interclass correlation coefficient. Results- The median CT ASPECTS for the clinicians was 5 (interquartile range, 4-7), for RAPID ASPECTS 3 (interquartile range, 1-6), and for DWI ASPECTS 3 (2-4). Median error for RAPID ASPECTS was 1 (interquartile range, -1 to 3) versus 3 (interquartile range, 1-4) for clinicians (P<0.001). The automated score had a higher level of agreement with the median of the DWI ASPECTS, both for the full scale and when dichotomized at <6 versus 6 or more (difference in intraclass correlation coefficient, P=0.001). Conclusions- RAPID ASPECTS was more accurate than experienced clinicians in identifying early evidence of brain ischemia as documented by DWI.
CRIS-Team WoS Importer:2019-11-12

Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery - Yoon, Seung Su; Preuhs, Elisabeth; Schmidt, Michaela; Forman, Christoph; Chitiboi, Teodora; Sharma, Puneet; Fernandes, Juliano L.; Tillmanns, Christoph; Wetzl, Jens; Maier, Andreas (Journal Machine Learning for Biomedical Imaging, 2023)

Automated Coronary Artery Ostia Detection in Magnetic Resonance Angiography - Stimpel, Bernhard; Forman, Christoph; Wetzl, Jens; Schmidt, Michaela; Maier, Andreas; Unberath, Mathias (Proceedings of the 25th Annual Meeting of the ISMRM, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa_3

Automated Curved and Multiplanar Reformation for Screening of the Proximal Coronary Arteries in MR Angiography - Stimpel, Bernhard; Wetzl, Jens; Forman, Christoph; Schmidt, Michaela; Maier, Andreas; Unberath, Mathias (Journal of Imaging, 2018)
Congenital anomalies of the coronary ostia can lead to sudden death. A screening solution would be useful to prevent adverse outcomes for the affected individuals. To be considered for integration into clinical routine, such a procedure must meet strict constraints in terms of invasiveness, time and user interaction. Imaging must be fast and seamlessly integrable into the clinical process. Non-contrast enhanced coronary magnetic resonance angiography (MRA) is well suited for this. Furthermore, planar reformations proved effective to reduce the acquired volumetric datasets to 2D images. These usually require time consuming user interaction, though. To fulfill the aforementioned challenges, we present a fully automated solution for imaging and reformatting of the proximal coronary arteries which enables rapid screening of these. The proposed pipeline consists of: (I) highly accelerated single breath-hold MRA data acquisition, (II) coronary ostia detection and vessel centerline extraction, and (III) curved planar reformation of the proximal coronary arteries, as well as multiplanar reformation of the coronary ostia. The procedure proved robust and effective in ten volunteer data sets. Imaging of the proximal coronary arteries took 24±5 s and was successful within one breath-hold for all patients. The extracted centerlines achieve an overlap of 0.76±0.18 compared to the reference standard and the average distance of the centerline points from the spherical surface for reformation was 1.1±0.51 mm. The promising results encourage further experiments on patient data, particularly in coronary ostia anomaly screening.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.automa_94
Keywords: Computer-aided detection and diagnosis; Coronary artery; Magnetic resonance angiography; Planar reformation

Automated Deep-Learning-based Inversion Time Selection for Cardiac Late Gadolinium Enhancement Imaging - Yoon, Seung Su; Schmidt, Michaela; Wintersperger, Bernd J.; Chitiboi, Teodora; Sharma, Puneet; Tillmanns, Christoph; Maier, Andreas; Wetzl, Jens (International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition, 2020)

Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests. - Lorch, Benedikt; Vaillant, Ghislain; Baumgartner, Christian; Bai, Wenja; Rueckert, Daniel; Maier, Andreas (Journal of Medical Engineering, 2017)
The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.
Keywords: MRI; bulk head motion; respiratory motion; k-space; sampling trajectories; random decision forests

Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests - Lorch, Benedikt; Vaillant, Ghislain; Baumgartner, Christian; Bai, Wenjia; Rueckert, Daniel; Maier, Andreas (Journal of Medical Engineering, 2017)
UnivIS-Import:2017-11-06:Pub.2017.tech.IMMD.IMMD5.automa_8
Keywords: image reconstruction; artifacts; classification

Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images - Fragoso-Garcia, Marco; Wilm, Frauke; Bertram, Christof A.; Merz, Sophie; Schmidt, Anja; Donovan, Taryn; Fuchs-Baumgartinger, Andrea; Bartel, Alexander; Marzahl, Christian; Diehl, Laura; Puget, Chloe; Maier, Andreas; et al. (Veterinary Pathology, 2023)
Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation (n = 35 WSIs), and test sets (n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types.
CRIS-Team Scopus Importer:2023-08-11
Keywords: computational pathology; computer-aided diagnosis; digital pathology; dog; machine learning; skin; veterinary oncology

Automated Left Ventricle Segmentation in 2-D LGE-MRI - Kurzendorfer, Tanja; Brost, Alexander; Forman, Christoph; Maier, Andreas (Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa_2
Keywords: Heart; Image Segmentation; Magnetic Resonance Imaging (MRI)

Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation - Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas (STACOM 2019, 2019)
Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients. Convolutional neural network (CNN) based methods for cardiac magnetic resonance (CMR) image segmentation rely on supervision with pixel-level annotations, and may not generalize well to images from a different domain. These methods are typically sensitive to variations in imaging protocols and data acquisition. Since annotating multi-sequence CMR images is tedious and subject to inter- and intra-observer variations, developing methods that can automatically adapt from one domain to the target domain is of great interest. In this paper, we propose an approach for domain adaptation in multi-sequence CMR segmentation task using transfer learning that combines multi-source image information. We first train an encoder-decoder CNN on T2-weighted and balanced-Steady State Free Precession (bSSFP) MR images with pixel-level annotation and fine-tune the same network with a limited number of Late Gadolinium Enhanced-MR (LGE-MR) subjects, to adapt the domain features. The domain-adapted network was trained with just four LGE-MR training samples and obtained an average Dice score of 85.0% on the test set comprises of 40 LGE-MR subjects. The proposed method significantly outperformed a network without adaptation trained from scratch on the same set of LGE-MR training data. <br

Automated postlaryngectomy telephone test - Haderlein, Tino; Riedhammer, Korbinian Thomas; Maier, Andreas; Nöth, Elmar; Eysholdt, Ulrich; Rosanowski, Frank (HNO, 2009)
Objective: In this study, an objective version of the postlaryngectomy telephone test (PLTT) for measuring speech intelligibility based on automatic speech recognition is presented. Methods: Thirty-one patients with tracheoesophageal substitute voice (25 men and six women, 63.4±8.7 years) were evaluated by 11 naïve listeners. The automatic measurement of speech intelligibility was expressed by means of word accuracy and word recognition rates, or the percentage of correctly recognized words from a word sequence. These automatic measures were compared with the subjectively obtained PLTT values. Results: The average PLTT intelligibility of the 11 naïve listeners was 47%; the automatically obtained word accuracy and word recognition rates were much lower (approximately 0% and 15%, respectively). The correlation between subjective and automatic evaluation, however, reached more than 0.9 in some of the examined cases. Conclusion: Automatic speech recognition provides an efficient, objective measure that is equivalent to the overall PLTT intelligibility value. © 2009 Springer Medizin Verlag.
Keywords: Automated pattern recognition; Correlation of data; Evaluation methodology; Speech intelligibility; Speech recognition software

Automated Protocoling for MRI Exams-Challenges and Solutions - Denck, Jonas; Haas, Oliver; Guehring, Jens; Maier, Andreas; Rothgang, Eva (Journal of Digital Imaging, 2022)
Automated protocoling for MRI examinations is an amendable target for workflow automation with artificial intelligence. However, there are still challenges to overcome for a successful and robust approach. These challenges are outlined and analyzed in this work. Through a literature review, we analyzed limitations of currently published approaches for automated protocoling. Then, we assessed these limitations quantitatively based on data from a private radiology practice. For this, we assessed the information content provided by the clinical indication by computing the overlap coefficients for the sets of ICD-10-coded admitting diagnoses of different MRI protocols. Additionally, we assessed the heterogeneity of protocol trees from three different MRI scanners based on the overlap coefficient, on MRI protocol and sequence level. Additionally, we applied sequence name standardization to demonstrate its effect on the heterogeneity assessment, i.e., the overlap coefficient, of different protocol trees. The overlap coefficient for the set of ICD-10-coded admitting diagnoses for different protocols ranges from 0.14 to 0.56 for brain/head MRI exams and 0.04 to 0.57 for spine exams. The overlap coefficient across the set of sequences used at two different scanners increases when applying sequence name standardization (from 0.81/0.86 to 0.93). Automated protocoling for MRI examinations has the potential to reduce the workload for radiologists. However, an automated protocoling approach cannot be solely based on admitting diagnosis as it does not provide sufficient information. Moreover, sequence name standardization increases the overlap coefficient across the set of sequences used at different scanners and therefore facilitates transfer learning.
CRIS-Team WoS Importer:2022-09-09

Automated segmentation of 3D cine cardiovascular magnetic resonance imaging - Tayebi Arasteh, Soroosh; Romanowicz, Jennifer; Pace, Danielle F.; Golland, Polina; Powell, Andrew J.; Maier, Andreas; Truhn, Daniel; Brosch, Tom; Weese, Juergen; Lotfinia, Mahshad; van der Geest, Rob J.; Moghari, Mehdi H. (Frontiers in Cardiovascular Medicine, 2023)
Introduction: As the life expectancy of children with congenital heart disease (CHD) is rapidly increasing and the adult population with CHD is growing, there is an unmet need to improve clinical workflow and efficiency of analysis. Cardiovascular magnetic resonance (CMR) is a noninvasive imaging modality for monitoring patients with CHD. CMR exam is based on multiple breath-hold 2-dimensional (2D) cine acquisitions that should be precisely prescribed and is expert and institution dependent. Moreover, 2D cine images have relatively thick slices, which does not allow for isotropic delineation of ventricular structures. Thus, development of an isotropic 3D cine acquisition and automatic segmentation method is worthwhile to make CMR workflow straightforward and efficient, as the present work aims to establish. Methods: Ninety-nine patients with many types of CHD were imaged using a non-angulated 3D cine CMR sequence covering the whole-heart and great vessels. Automatic supervised and semi-supervised deep-learning-based methods were developed for whole-heart segmentation of 3D cine images to separately delineate the cardiac structures, including both atria, both ventricles, aorta, pulmonary arteries, and superior and inferior vena cavae. The segmentation results derived from the two methods were compared with the manual segmentation in terms of Dice score, a degree of overlap agreement, and atrial and ventricular volume measurements. Results: The semi-supervised method resulted in a better overlap agreement with the manual segmentation than the supervised method for all 8 structures (Dice score 83.23 ± 16.76% vs. 77.98 ± 19.64%; P-value ≤0.001). The mean difference error in atrial and ventricular volumetric measurements between manual segmentation and semi-supervised method was lower (bias ≤ 5.2 ml) than the supervised method (bias ≤ 10.1 ml). Discussion: The proposed semi-supervised method is capable of cardiac segmentation and chamber volume quantification in a CHD population with wide anatomical variability. It accurately delineates the heart chambers and great vessels and can be used to accurately calculate ventricular and atrial volumes throughout the cardiac cycle. Such a segmentation method can reduce inter- and intra- observer variability and make CMR exams more standardized and efficient.
Keywords: congenital heart disease; deep learning; 3D cine; CMR image analysis; automatic segmentation

Automated Shoe Metrology by X-Ray Computed Tomography - Leipert, Martin; Herl, Gabriel; Müller, Michael; Meskemper, Joshua; Zabler, Simon (3DBODY.TECH 2023 - 14th International Conference and Exhibition on 3D Body Scanning and Processing Technologies, 2023)

Automated Time Adjusted Myocardial Inversion Time Selection for Late Gadolinium Enhancement Imaging – Validation and Application on Patient Datasets - Yoon, Seung Su; Schmidt, Michaela; Rick, Manuela; Chitiboi, Teodora; Sharma, Puneet; Emrich, Tilman; Tillmanns, Christoph; Seitz, Andreas; Mahrholdt, Heiko; Toupin, Solenn; Pezel, Théo; Garot, Jérome; et al. (Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, 2022)

Automatically evaluated degree of intelligibility of children with different cleft type from preschool and elementary school measured by automatic speech recognition - Schuster, Maria; Maier, Andreas; Bocklet, Tobias; Nkenke, Emeka; Holst, Alexandra; Eysholdt, Ulrich; Stelzle, Florian (International Journal of Pediatric Otorhinolaryngology, 2012)

Automatic Assessment of Alzheimer's across Three Languages Using Speech and Language Features - Pérez Toro, Paula Andrea; Arias Vergara, Tomás; Braun, F.; Hönig, F.; Tobón-Quintero, C. A.; Aguillón, D.; Lopera, F.; Hincapié-Henao, L.; Schuster, M.; Riedhammer, K.; Maier, Andreas; Nöth, Elmar; et al. (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2023)
With the increasing prevalence of Alzheimer's Disease (AD) worldwide, it is essential to develop non-invasive methods to monitor the progression of the disease. Speech and language analyses are suitable for detecting the cognitive impairment of AD patients; thus, by analyzing changes in speech patterns and language use, researchers can develop methods to monitor AD remotely. In this paper, we investigated several speech and language techniques commonly used for the automatic detection of AD. Furthermore, we considered speech recordings of 448 patients in three different languages: Spanish (57), German (205), and English (186). Cross-lingual analysis was carried out using two classification approaches: (1) training/testing in one or more languages and (2) training in one language and testing in another. We obtained unweighted average recall values of up to 83% to classify AD using the first classification approach and up to 70% with the second.
CRIS-Team Scopus Importer:2023-09-29
Keywords: Alzheimer's Disease; Cross-lingual Analysis; Deep Learning; Dementia; Pathological Speech Processing

Automatic Assessment of Children's Speech with Cleft Lip and Palate - Maier, Andreas; Nöth, Elmar; Nkenke, Emeka; Schuster, Maria (Language Technologies, IS-LTC 2006, 2006)

Automatic CAD-RADS Scoring using Deep Learning - Denzinger, Felix; Wels, Michael; Breininger, Katharina; Gülsün, Mehmet A.; Schöbinger, Max; Andre, Florian; Buss, Sebastian J.; Goerich, Johannes; Sühling, Michael; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020)
Coronary CT angiography (CCTA) has established its role as a non-invasive modality for the diagnosis of coronary artery disease (CAD). The CAD-Reporting and Data System (CAD-RADS) has been developed to standardize communication and aid in decision making based on CCTA findings. The CAD-RADS score is determined by manual assessment of all coronary vessels and the grading of lesions within the coronary artery tree. We propose a bottom-up approach for fully-automated prediction of this score using deep-learning operating on a segment-wise representation of the coronary arteries. The method relies solely on a prior fully-automated centerline extraction and segment labeling and predicts the segment-wise stenosis degree and the overall calcification grade as auxiliary tasks in a multi-task learning setup. We evaluate our approach on a data collection consisting of 2,867 patients. On the task of identifying patients with a CAD-RADS score indicating the need for further invasive investigation our approach reaches an area under curve (AUC) of 0.923 and an AUC of 0.914 for determining whether the patient suffers from CAD. This level of performance enables our approach to be used in a fully-automated screening setup or to assist diagnostic CCTA reading, especially due to its neural architecture design – which allows comprehensive predictions.<br
Keywords: Coronary Artery Disease; Coronary CT Angiography; Deep Learning; Data Representation; CAD-RADS

Automatic Cardiac Resting Phase Detection for Static Cardiac Imaging Using Deep Neural Networks - Yoon, Seung Su; Hoppe, Elisabeth; Schmidt, Michaela; Forman, Christoph; Sharma, Puneet; Tillmanns, Christoph; Maier, Andreas; Wetzl, Jens (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (27th Annual Meeting & Exhibition), 2019)

Automatic Cell Detection in Bright-Field Microscope Images Using SIFT, Random Forests, and Hierarchical Clustering - Mualla, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2013)

Automatic Chain Line Segmentation in Historical Prints - Biendl, Meike; Sindel, Aline; Klinke, Thomas; Maier, Andreas; Christlein, Vincent (Pattern Recognition. ICPR International Workshops and Challenges, 2021)
The analysis of chain line patterns in historical prints can provide valuable information about the origin of the paper. For this task, we propose a method to automatically detect chain lines in transmitted light images of prints from the 16th century. As motifs and writing on the paper partially occlude the paper structure, we utilize a convolutional neural network in combination with further postprocessing steps to segment and parametrize the chain lines. We compare the number of parametrized lines, as well as the distances between them, with reference lines and values. Our proposed method is an effective method showing a low error of less than 1 mm in comparison to the manually measured chain line distances.<br
Keywords: Line segmentation; Line detection; Chain lines; Historical prints; Paper structure

Automatic Classification and Pathological Staging of Confocal Laser Endomicroscopic Images of the Vocal Cords - Vo, Kim; Jaremenko, Christian; Bohr, Christopher; Neumann, Helmut; Maier, Andreas (Bildverarbeitung für die Medizin 2017 Algorithmen Systeme Anwendungen, 2017)
Confocal laser endomicroscopy is a novel imaging technique which provides real-time in vivo examination and histological analysis of tissue during an ongoing endoscopy. We present an automatic classification system that is able to differentiate between healthy and cancerous tissue of the vocal cords. Textural as well as CNN features are encoded using Fisher vectors and vector of locally aggregated descriptors while the classification is performed using random forests and support vector machines. Two experiments are investigated following a leave-one-sequence-out cross-validation and a fixed training and test set approach. Classification rates reach up to 87.6 % and 81.5 %, respectively.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa_7

Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning - Aubreville, Marc; Knipfer, Christian; Oetter, Nicolai; Jaremenko, Christian; Rodner, Erik; Denzler, Joachim; Bohr, Christopher; Neumann, Helmut; Stelzle, Florian; Maier, Andreas (Scientific Reports, 2017)
Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, su cient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 speci c locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, speci city 90%).
Keywords: Cancer imaging; translational research; deep learning

Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images - Deitsch, Sergiu; Christlein, Vincent; Berger, Stephan; Buerhop-Lutz, Claudia; Maier, Andreas; Gallwitz, Florian; Riess, Christian (Solar Energy, 2019)
Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules. However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge of many different types of defects. In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell. The approaches differ in their hardware requirements, which are dictated by their respective application scenarios. The more hardware-efficient approach is based on hand-crafted features that are classified in a Support Vector Machine (SVM). To obtain a strong performance, we investigate and compare various processing variants. The more hardware-demanding approach uses an end-to-end deep Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit (GPU). Both approaches are trained on 1,968 cells extracted from high resolution EL intensity images of mono- and polycrystalline PV modules. The CNN is more accurate, and reaches an average accuracy of 88.42%. The SVM achieves a slightly lower average accuracy of 82.44%, but can run on arbitrary hardware. Both automated approaches make continuous, highly accurate monitoring of PV cells feasible.<br
CRIS-Team Scopus Importer:2019-05-09
Keywords: Deep learning; defect classification; electroluminescence imaging; photovoltaic modules; regression analysis; support vector machines; visual inspection

Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging - Schlereth, Maja; Stromer, Daniel; Breininger, Katharina; Wagner, Alexandra; Tan, Lina; Maier, Andreas; Knieling, Ferdinand (Informatik aktuell, 2022)
Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability. The NMDs evaluated in this study often manifest in early childhood. As subtypes of disease, e.g. Duchenne muscular dystropy (DMD) and spinal muscular atrophy (SMA), are difficult to differentiate at the beginning and worsen quickly, fast and reliable differential diagnosis is crucial. Photoacoustic and ultrasound imaging has shown great potential to visualize and quantify the extent of different diseases. The addition of automatic classification of such image data could further improve standard diagnostic procedures.We compare deep learning-based 2-class and 3-class classifiers based on VGG16 for differentiating healthy from diseased muscular tissue. This work shows promising results with high accuracies above 0.86 for the 3-class problem and can be used as a proof of concept for future approaches for earlier diagnosis and therapeutic monitoring of NMDs.<br
CRIS-Team Scopus Importer:2022-05-06

Automatic Classification of Reading Disorders in a Single Word Reading Test - Maier, Andreas; Horndasch, Stefanie; Nöth, Elmar (Workshop on Child, Computer, and Interaction 2009, 2009)

Automatic Dating of Historical Documents - Christlein, Vincent; Gropp, Martin; Maier, Andreas (Kodikologie und Paläographie im digitalen Zeitalter, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa_32

Automatic dementia screening and scoring by applying deep learning on clock-drawing tests - Chen, Shuqing; Stromer, Daniel; Alnasser Alabdalrahim, Harb; Schwab, Stefan; Weih, Markus; Maier, Andreas (Scientific Reports, 2020)
Dementia is one of the most common neurological syndromes in the world. Usually, diagnoses are made based on paper-and-pencil tests and scored depending on personal judgments of experts. This technique can introduce errors and has high inter-rater variability. To overcome these issues, we present an automatic assessment of the widely used paper-based clock-drawing test by means of deep neural networks. Our study includes a comparison of three modern architectures: VGG16, ResNet-152, and DenseNet-121. The dataset consisted of 1315 individuals. To deal with the limited amount of data, which also included several dementia types, we used optimization strategies for training the neural network. The outcome of our work is a standardized and digital estimation of the dementia screening result and severity level for an individual. We achieved accuracies of 96.65% for screening and up to 98.54% for scoring, overcoming the reported state-of-the-art as well as human accuracies. Due to the digital format, the paper-based test can be simply scanned by using a mobile device and then be evaluated also in areas where there is a staff shortage or where no clinical experts are available.<br

Automatic Detection and Analysis of Photovoltaic Modules in Aerial Infrared Imagery - Dotenco, Sergiu; Dalsaß, Manuel; Winkler, Ludwig; Wurzner, Tobias; Brabec, Christoph; Maier, Andreas; Gallwitz, Florian (IEEE Winter Conference on Applications of Computer Vision, 2016)
Drone-based aerial thermography has become a convenient quality assessment tool for the precise localization of defective modules and cells in large photovoltaic-power plants. However, manual evaluation of aerial infrared recordings can be extremely time-consuming. Therefore, we propose an approach for automatic detection and analysis of photovoltaic modules in aerial infrared images. Significant temperature abnormalities such as hot spots and hot areas can be identified using our processing pipeline. To identify such defects, we first detect the individual modules in infrared images, and then use statistical tests to detect the defective modules. A quantitative evaluation of the detection and analysis pipeline on real-world, infrared recordings shows the applicability of our approac

Automatic detection of articulation disorders in children with cleft lip and palate - Maier, Andreas; Hönig, Florian Thomas; Bocklet, Tobias; Nöth, Elmar; Stelzle, Florian; Nkenke, Emeka; Schuster, Maria (Journal of the Acoustical Society of America, 2009)

Automatic detection of capillary dilation and looping in patients with diabetic retinopathy from optical coherence tomography angiography data - Husvogt, Lennart; Arya, Malvika; Alibhai, A. Yasin; Moult, Eric M.; Duker, Jay S.; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (ARVO Annual Meeting 2018, 2018)
Purpose : Diabetic retinopathy (DR) is one the most common causes of blindness in the western world. Microaneuryms are the hallmark of diabetic retinopathy and are almost universally present in early DR. We present an algorithm for the automatic detection of pathologic capillary dilation and looping as an aide in the diagnosis of microaneurysms. Methods : Patients were enrolled at the New England Eye Center at Tufts Medical Center in Boston. Optical coherence tomography angiography (OCTA) data were acquired from 8 patients with varying degrees of DR using the OptoVue Avanti device. 16 OCTA volumes with field sizes of 3x3 mm were acquired in each imaging session. The Avanti software was used to segment the retinal layers end generate en face projections. The en face projections of the superficial and deep capillary plexi were used for the training of the proposed algorithm. A standard machine learning algorithm (random forest) was used to train the automatic detection of capillary dilation in superficial and deep layers based on features in the projections. The training data were generated by an expert grader at the Boston Imaging Reading Center and vascular anomalies such as dilation and looping were labeled. Of the 16 data sets, 5 were selected at random as test sets. Results : Of the test data, 99% of all pixels are correctly labeled by the algorithm as either vascular dilation or normal. Figure 1 shows an example detection result from the superficial capillary layer. Details are provided in the figure caption. Conclusions : This novel approach suggests that it is possible to fully automate the detection of vascular abnormalities which may correlate to microaneurysms and thus early stages of diabetic retinopathy. A fully automated detection of this type of vascular anomaly has not been demonstrated yet to the best of our knowledg

Automatic Detection of Free Intra-Abdominal Air in Computed Tomography - Taubmann, Oliver; Li, Jingpeng; Denzinger, Felix; Eibenberger, Eva; Müller, Felix; Brejnebøl, Mathias; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020)
Pneumoperitoneum, the presence of air within the peritoneal cavity, is a comparatively rare but potentially urgent critical finding in patients presenting with acute abdominal pain. When prior laparoscopic treatment can be ruled out as a cause, it can indicate perforation of the wall of a hollow organ, which typically necessitates immediate surgery. Computed tomography (CT) is the gold standard for detecting free intraabdominal air, yet subtle cases are easy to miss. More crucially though, if there is no initial suspicion of pneumoperitoneum, the scans may not be read immediately as other emergency patients take precedence. Therefore, fully automatic detection would provide a direct clinical benefit. In this work, an algorithm for this purpose is proposed which follows a sliding-window approach and has a deep-learning based classifier at its core. In addition to the baseline method, variants that rely on multi-scale inputs and recurrent layers to increase robustness are presented. In a fivefold cross validation on the training data, consisting in abdominal CT scans of 110 affected patients and 29 controls, our method achieved an area under the receiver-operating characteristic curve of 89 % for caselevel classification. Due to its high specificity at reasonable detection rates, it shows potential for use in triage, where false alerts are considered particularly harmful as they may disrupt the clinical workflow.<br
Keywords: Pneumoperitoneum · Free Intra-Abdominal Air · CT

Automatic Detection of Illuminated Charters - Christlein, Vincent (Illuminierte Urkunden. Beiträge aus Diplomatik, Kunstgeschichte und Digital Humanities/Illuminated Charters. Essays from Diplomatic, Art History and Digital Humanities, 2018)
This paper focuses on automatic document classification. Documents are assigned to different levels based on the degree of illumination of a document. A method ori- ginally proposed for writer identification is adapted for and evaluated in a dataset containing illuminated charters. First results show that the classification of charters in “pure text” versus “contains some form of decoration/illumination” is successful in 83.7% of the cases, which might be enough for a rough pre-selection of illuminated charters. A more refined classification into three subcategories of illuminated charters gives an average accuracy of 76.5%. A detailed inspection reveals that one category in particular, referring to “unusually rich decoration”, is difficult for an automatic system to classif
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.automa_9

Automatic detection of inertial sensor orientation for movement analysis in Parkinsons disease - Maier, Jennifer; Reinfelder, Samuel; Barth, Jens; Klucken, Jochen; Eskofier, Björn (BSA Conference 2013 - Biosignal Analysis, 2013)
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.automa_27

Automatic Detection of Ostia in the Left Atrium - Hoffmann, Matthias; Koch, Martin; Strobel, Norbert; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.automa

Automatic Detection of Sigmatism in Children - Botinhao, Cassia; Degenkolb-Weyers, Sabine; Maier, Andreas; Nöth, Elmar; Eysholdt, Ulrich; Bocklet, Tobias (WOCCI 2012 - Workshop on Child, Computer and Interaction, 2012)

Automatic Detection of Stent Graft Markers in 2-D Fluoroscopy Images - Reiml, Sabrina; Pfister, Marcus; Toth, Daniel; Maier, Andreas; Hoffmann, Matthias; Kowarschik, Markus; Hornegger, Joachim (Joint MICCAI workshop on Computing and Visualisation for Intravascular Imaging and Computer-Assisted Stenting, 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.automa_90

Automatic Evaluation of Characteristic Speech Disorders in Children with Cleft Lip and Palate - Maier, Andreas; Hönig, Florian Thomas; Hacker, Christian; Schuster, Maria; Nöth, Elmar (Interspeech 2008, 2008)

Automatic Evaluation of Dysarthric Speech and Telemedical Use in the Therapy - Nöth, Elmar; Maier, Andreas; Gebhard, Arnd; Bocklet, Tobias; Schupp, Wilfried; Schuster, Maria; Haderlein, Tino (The Phonetician, 2011)

Automatic Evaluation of Pathologic Speech - from Research to Routine Clinical Use - Nöth, Elmar; Maier, Andreas; Haderlein, Tino; Riedhammer, Korbinian Thomas; Rosanowski, Frank; Schuster, Maria (Text, Speech and Dialogue, 2007)

Automatic Finger Joint Detection for Volumetric Hand Imaging - Bopp, Johannes; Unberath, Mathias; Steidl, Stefan; Fahrig, Rebecca; Oliveira, Isabelle; Kleyer, Arnd; Maier, Andreas (Bildverarbeitung für Medizin, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.automa_9

Automatic Font Group Recognition in Early Printed Books - Weichselbaumer, Nikolaus; Seuret, Mathias; Limbach, Saskia; Christlein, Vincent; Maier, Andreas (6. Tagung des Verbands Digital Humanities im deutschsprachigen Raum e.V., 2019)

Automatic histogram-based initialization of K-means clustering in CT - Tian, Mengqiu; Yang, Qiao; Maier, Andreas; Schasiepen, Ingo; Maass, Nicole; Elter, Matthias (Informatik aktuell, 2013)
K-means clustering [1] has been widely used in various applications. One intrinsic limitation in K-means clustering is that the choice of initial clustering centroids may highly influence the performance of the algorithm. Some existing K-means initialization algorithms could generally achieve good results. However, in certain cases, such as CT images that contain several materials with similar gray-levels, such existing initialization algorithms will lead to poor performance in distinguishing those materials. We propose an automatic K-means initialization algorithm based on histogram analysis, which manages to overcome the aforementioned deficiency. Results demonstrate that our method achieves high efficiency in terms of finding starting points close to ground truth so that offers reliable segmentation results for CT images in aforementioned situation.
CRIS-Team Scopus Importer:2022-08-05

Automatic identification of Microaneurysms in Diabetic Retinopathy patients utilizing an nn-UNet - Lam, Kenneth; Husvogt, Lennart; Yaghy, Antonio; Camacho, Alex; Maier, Andreas; Waheed, Nadia K. (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2022)
CRIS-Team WoS Importer:2022-09-30

Automatic Layer Generation for Scar Transmurality Visualization - Reiml, Sabrina; Kurzendorfer, Tanja; Toth, Daniel; Mountney, Peter; Panayiotou, Maria; Behar, Jonathan M.; Rinaldi, Christopher A.; Rhode, Kawal; Maier, Andreas; Brost, Alexander (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa

Automatic malignancy estimation for pulmonary nodules from CT images - Mentl, Katrin; Saffoury, Rimon; Maier, Andreas (Informatik aktuell, 2018)
Early detection of lung cancer is crucial to increase the chance of cure. As lung cancer often manifests itself in the presence of malignant pulmonary nodules, the assessment of such is of high clinical importance. Lung cancer screening is primarily performed using diagnostic imaging modalities such as CT, while invasive methods such as biopsy are used as a last resort to confirm diagnosis.
CRIS-Team Scopus Importer:2022-06-05

Automatic Motion Estimation and Compensation Framework forWeight-bearing C-arm CT scans using Fiducial Markers - Müller, Kerstin; Berger, Martin; Choi, Jang-Hwan; Maier, Andreas; Fahrig, Rebecca (IFMBE Proceedings, 2015)
UnivIS-Import:2018-09-06:Pub.2015.tech.IMMD.IMMD5.automa_2

Automatic Multi-organ Segmentation in Dual Energy CT (DECT) with Dedicated 3D Fully Convolutional DECT Networks - Chen, Shuqing; Zhong, Xia; Hu, Shiyang; Dorn, Sabrina; Kachelriess, Marc; Lell, Michael; Maier, Andreas (Medical Physics, 2020)
Purpose Dual‐energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissues compared to conventional single‐energy CT (SECT). Recent research shows that automatic multi‐organ segmentation of DECT data can improve DECT clinical applications. However, most segmentation methods are designed for SECT, while DECT has been significantly less pronounced in research. Therefore, a novel approach is required that is able to take full advantage of the extra information provided by DECT. Methods In the scope of this work, we proposed four three‐dimensional (3D) fully convolutional neural network algorithms for the automatic segmentation of DECT data. We incorporated the extra energy information differently and embedded the fusion of information in each of the network architectures. Results Quantitative evaluation using 45 thorax/abdomen DECT datasets acquired with a clinical dual‐source CT system was investigated. The segmentation of six thoracic and abdominal organs (left and right lungs, liver, spleen, and left and right kidneys) were evaluated using a fivefold cross‐validation strategy. In all of the tests, we achieved the best average Dice coefficients of 98% for the right lung, 98% for the left lung, 96% for the liver, 92% for the spleen, 95% for the right kidney, 93% for the left kidney, respectively. The network architectures exploit dual‐energy spectra and outperform deep learning for SECT. Conclusions The results of the cross‐validation show that our methods are feasible and promising. Successful tests on special clinical cases reveal that our methods have high adaptability in the practical application.
Keywords: Multi-organ; DECT; FCN; U-Net; deep learning

Automatic Multi-Organ Segmentation in Dual Energy CT using 3D Fully Convolutional Network - Chen, Shuqing; Zhong, Xia; Hu, Shiyang; Dorn, Sabrina; Kachelriess, Marc; Lell, Michael; Maier, Andreas (MIDL, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.automa_98

Automatic Objective Analysis of Speech Disorders on Patients with Oral Squamous Cell Carcinoma - Stelzle, Florian; Schuster, Maria; Maier, Andreas; Bocklet, Tobias; Nöth, Elmar; Seiß, Martin; Neukam, Friedrich Wilhelm; Nkenke, Emeka (3rd International IZKF-Symposium, 2009)

Automatic orientation estimation of inertial sensors in C-Arm CT Projections - Thies, Mareike; Maier, Jennifer; Eskofier, Björn; Maier, Andreas; Levenston, Marc; Gold, Garry; Fahrig, Rebecca (Current Directions in Biomedical Engineering, 2019)
To obtain CT images of the knee joint in a more lifelike position, data acquisition can be performed with patients in standing rather than in lying position. However, in that situation, people tend to show involuntary motion. One possibility to compensate for that motion is the use of Inertial Measurement Units, that capture the accelerations during the scan. For this purpose, their local coordinate system needs to be known. An estimation based on the SIFT algorithm was implemented and compared to an existing approach that uses the Fast Radial Symmetry transform and to expert labels for evaluation. The SIFT method showed to be superior to the existing approach as it could extract stable feature points from the projections that were used to estimate the three-dimensional coordinate system in a reliable manner. The final algorithm achieved a mean euclidean distance of 2.61 mm between the calculated position of the origin and the assumed ground truth by the expert labels.
CRIS-Team Scopus Importer:2019-10-01
Keywords: Computed Tomography; Feature Extraction; Motion Compensated Reconstruction; SIFT

Automatic Path Planning for Safe Guide Pin Insertion in PCL Reconstruction Surgery - Kordon, Florian Johannes; Maier, Andreas; Swartman, Benedict; Privalov, Maxim; El Barbari, Jan Siad; Kunze, Holger (Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 2021)
Reconstruction surgery of torn ligaments typically requires precise and anatomically correct fixation of the graft substitute on the bone surface. Several planning methodologies have been proposed that aim at standardizing the interventional procedure by localizing drill sites or defining the drill tunnel orientation with the help of anatomical landmarks. However, the practical implementation is limited by the often complex and time-consuming nature of the planning steps. For this reason, we propose an automatic solution for safe guide pin path planning based on bone contour extraction, axis detection, anatomical landmark detection, and geometrical construction. We evaluate our approach for the task of double-bundle posterior cruciate ligament reconstruction surgery on the lateral tibia using 38 clinical X-ray images. Our method achieves a median path angulation error of 0.37∘0.37° and a median localization error of 0.96 mm for the ligament attachment center.<br
Keywords: Surgical planning; Orthopedics; X-ray imaging; Intra-operative guidance

Automatic Plane Adjustment in Surgical Cone Beam CT-volumes - Martín Vicario, Celia; Kordon, Florian Johannes; Denzinger, Felix; Weiten, Markus; Thomas, Sarina; Kausch, Lisa; Franke, Jochen; Keil, Holger; Maier, Andreas; Kunze, Holger (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Automatic plane adjustment of orthopedic intraoperative flat panel detector CT-volumes - Martín Vicario, Celia; Kordon, Florian Johannes; Denzinger, Felix; El Barbari, Jan Siad; Privalov, Maxim; Franke, Jochen; Thomas, Sarina; Kausch, Lisa; Maier, Andreas; Kunze, Holger (Journal of Medical Imaging, 2022)
Purpose: To assess the result in orthopedic trauma surgery, usually three-dimensional volume data of the treated region is acquired. With mobile C-arm systems, these acquisitions can be performed intraoperatively, reducing the number of required revision surgeries. However, the acquired volumes are typically not aligned to the anatomical regions. Thus, the multiplanar reconstructed (MPR) planes need to be adjusted manually during the review of the volume. To speed up and ease the workflow, an automatic parameterization of these planes is needed. Approach: We present a detailed study of multitask learning (MTL) regression networks to estimate the parameters of the MPR planes. First, various mathematical descriptions for rotation, including Euler angle, quaternion, and matrix representation, are revised. Then, two different MTL network architectures based on the PoseNet are compared with a single task learning network. Results: Using a matrix description rather than the Euler angle description, the accuracy of the regressed normals improves from 7.7 deg to 7.3 deg in the mean value for single anatomies. The multihead approach improves the regression of the plane position from 7.4 to 6.1 mm, whereas the orientation does not benefit from this approach. Thus, the achieved accuracy meets the reported interrater variance in similarly complex body regions of up to 6.3 deg for the normals and up to 9.3 mm for the plane position. Conclusions: The use of a multihead approach with shared features leads to more accurate plane regression compared with the use of individual networks for each task. It also improves the angle estimation for the ankle region. The reported results are in the same range as manual plane adjustments. The use of a combined network with shared parameters requires less memory, which is a great benefit for the implementation of an application for the surgical environmen

Automatic Plane Adjustment of Orthopedic Intraoperative Flat Panel Detector CT-Volumes - Martín Vicario, Celia; Kordon, Florian Johannes; Denzinger, Felix; Weiten, Markus; Thomas, Sarina; Kausch, Lisa; Franke, Jochen; Keil, Holger; Maier, Andreas; Kunze, Holger (Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020)
Flat panel computed tomography is used intraoperatively to assess the result of surgery. Due to workflow issues, the acquisition typically cannot be carried out in such a way that the axis aligned multiplanar reconstructions (MPR) of the volume match the anatomically aligned MPRs. This needs to be performed manually, adding additional effort during viewing the datasets. A PoseNet convolutional neural network (CNN) is trained such that parameters of anatomically aligned MPR planes are regressed. Different mathematical approaches to describe plane rotation are compared, as well as a cost function is optimized to incorporate orientation constraints. The CNN is evaluated on two anatomical regions. For one of these regions, one plane is not orthogonal to the other two planes. The plane’s normal can be estimated with a median accuracy of 5°, the in-plane rotation with an accuracy of 6°, and the position with an accuracy of 6 mm. Compared to state-of-the-art algorithms the labeling effort for this method is much lower as no segmentation is required. The computation time during inference is less than 0.05 s.<br
Keywords: Orthopedics; Flat planel CT; Multiplanar reconstruction

Automatic Quantification of Speech Intelligibility in Patients after Treatment for Oral Squamous Cell Carcinoma - Stelzle, Florian; Maier, Andreas; Nöth, Elmar; Bocklet, Tobias; Knipfer, Christian; Schuster, Maria; Neukam, Friedrich Wilhelm; Nkenke, Emeka (Journal of Maxillofacial and Oral Surgery, 2011)

Automatic Quantification of Speech Intelligibility of Adults with Oral Squamous Cell Carcinoma - Windrich, Martin; Maier, Andreas; Kohler, Regina; Nöth, Elmar; Nkenke, Emeka; Eysholdt, Ulrich; Schuster, Maria (Folia Phoniatrica Et Logopaedica, 2008)

Automatic Removal of Externally Attached Fiducial Markers in Cone Beam C-arm CT - Berger, Martin; Forman, Christoph; Schwemmer, Chris; Choi, Jang-Hwan; Müller, Kerstin; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung für die Medizin 2014, 2014)
In computed tomography fiducial markers are frequently used to obtain accurate point correspondences for further processing. These markers typically cause metal artefacts, decreasing image quality of the subsequent reconstruction and are therefore often removed from the projection data. The placement of such markers is usually done on a surface, separating two materials, e.g. skin and air. Hence, a correct restoration of the occluded area is difficult. In this work six state-of-the-art interpolation techniques for the removal of high-density fiducial markers from cone-beam CT projection data are compared. We conducted a qualitative and quantitative evaluation for the removal of such markers and the ability to reconstruct the adjoining edge. Results indicate that an iterative spectral deconvolution is best suited for this application, showing promising results in terms of edge, as well as noise restoration.
UnivIS-Import:2015-07-08:Pub.2014.tech.IMMD.IMMD5.automa_5
Keywords: GRK-1773; Interpolation; C-arm CT; Metal Artifact

Automatic Scoring of the Intelligibility in Patients with Cancer of the Oral Cavity - Maier, Andreas; Schuster, Maria; Batliner, Anton; Nöth, Elmar; Nkenke, Emeka (Interspeech 2007, 2007)

Automatic speech recognition (ASR) and its use as a tool for assessment or therapy of voice, speech, and language disorders - Kitzing, Peter; Maier, Andreas; Ahlander, Viveka Lyberg (Logopedics Phoniatrics Vocology, 2009)

Automatic Speech Recognition Systems for the Evaluation of Voice and Speech Disorders in Head and Neck Cancer - Maier, Andreas; Haderlein, Tino; Stelzle, Florian; Nöth, Elmar; Nkenke, Emeka; Rosanowski, Frank; Schützenberger, Anne; Schuster, Maria (EURASIP Journal on Audio, Speech, and Music Processing, 2010)

Automatic Switching of Organ Programs in Interventional X-ray Machines Using Deep Learning - Ravi, Arpitha; Kordon, Florian Johannes; Maier, Andreas (Bildverarbeitung für die Medizin 2022, 2022)
In interventional radiology, the optimal parametrization of the X-ray image and any subsequent software processing strongly depends on the body region being imaged. These anatomy-specific parameters are combined to create customized organ programs and are necessary to obtain an optimal image quality. In today’s workflow, these programs have to be switched manually by the surgeon, which can be complex. This paper investigates a deep learning algorithm for automatic switching of organ programs in interventional X-ray machines based on the automatic detection of the imaged anatomy. We compare multiple network architectures for cardiac anatomy classification where the algorithm has to differentiate the left coronary artery, right coronary artery, and left ventricle on radiographs without contrast medium. The best-performing model achieves a micro average F1-score of 0.80. A comparison of the model performance with expert rater annotations shows promising results and recommends further clinical evaluation.
Keywords: Medical Imaging; Organ Program; X-ray Guidance

Automatic thrombus detection in non-enhanced computed tomography images in patients with acute ischemic stroke - Löber, Patrick; Stimpel, Bernhard; Syben-Leisner, Christopher; Maier, Andreas; Ditt, Hendrik; Schramm, Peter; Raczkowski, Boy; Kemmling, Andre (Eurographics Proceedings 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa_9

Automatic Vertebrae Segmentation in Fluoroscopic Images for Electrophysiology - Reiml, Sabrina; Kurzendorfer, Tanja; Toth, Daniel; Mountney, Peter; Steidl, Stefan; Brost, Alexander; 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.automa_4

Automatic Writer Identification in Historical Documents: A Case Study - Christlein, Vincent; Diem, Markus; Kleber, Florian; Mühlberger, Günter; Schwägerl-Melchior, Verena; van Gelder, Esther; Maier, Andreas (Zeitschrift für digitale Geisteswissenschaften, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.automa_7

Automatic Writer Identification in Medieval Papal Charters - Christlein, Vincent; Angelopoulou, Elli (Papsturkunden des frühen und hohen Mittelalters, 2015)
UnivIS-Import:2017-01-09:Pub.2015.tech.IMMD.IMMD5.automa_6

Automating Time-Consuming and Error-Prone Manual Nursing Management Documentation Processes - Haas, Oliver; Hutzler, Michaela; Egginger, Thomas H.; Maier, Andreas; Rothgang, Eva (CIN - Computers, Informatics, Nursing, 2021)
A German regulation requires nursing managers to document patient-nurse ratios. They have to combine heterogeneous hospital data from different sources. Missing documentation or ratios that are too high lead to sanctions. Automated approaches are needed to accelerate the time-consuming and error-prone documentation process. A documentation and visualization system was implemented. The system allows nursing managers to quickly and automatically create the documentation required by the regulation. Interactive visualization dashboards assist with the analysis of patient and staff numbers. The developed method was effectively used in nursing management tasks. No changes to the information technology infrastructure were needed. The new process is around 35 hours per month faster and less error-prone. The documentation functionality automatically reads the required information and correctly calculates the documentation. The visualization functionality allows nursing managers to assess the current patient-nurse ratios before the documentation is submitted. The method scales to multiple wards and locations. It calculates the sanctions to expect and is easily updatable. The proposed method is expected to decrease nursing administration workloads and facilitate the analysis of nursing management data in a cost-effective way.

Automatische Bewertung der Nasalitaet von Kindersprache - Maier, Andreas; Nöth, Elmar; Wohlleben, Ulrike; Eysholdt, Ulrich; Schuster, Maria (Aktuelle phoniatrische Aspekte, 2007)

Automatische internetbasierte Evaluation der Verständlichkeit - Maier, Andreas; Haderlein, Tino; Hacker, Christian; Nöth, Elmar; Rosanowski, Frank; Eysholdt, Ulrich; Schuster, Maria (Aktuelle phoniatrisch-pädaudiologische Aspekte 2006, 2006)

Automatische Typenbestimmung in historischen Drucken - Weichselbaumer, Nikolaus; Christlein, Vincent (DHd 2016, Modellierung - Vernetzung - Visualisierung Die Digital Humanities als fächerübergreifendes Forschungsparadigma, 2016)
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.automa_3

Automatische Verständlichkeitsanalyse bei Kindern mit Lippen- und Lippenkieferspalten mittels Spracherkennung - Maier, Andreas; Nöth, Elmar; Eysholdt, Ulrich; Schuster, Maria (Aktuelle phoniatrisch-pädaudiologische Aspekte 2009, 2009)

Automatische Verständlichkeitsbewertung von Telefonaufnahmen Larynxteilresezierter mittels prosodischer Analyse - Haderlein, Tino; Maier, Andreas; Nöth, Elmar; Rosanowski, Frank; Eysholdt, Ulrich (Aktuelle phoniatrisch-pädaudiologische Aspekte 2010, 2010)

Automatisierung des Postlaryngektomie-Telefontests - Haderlein, Tino; Riedhammer, Korbinian Thomas; Maier, Andreas; Nöth, Elmar; Eysholdt, Ulrich; Rosanowski, Frank (HNO, 2009)

Auto-tuned Path-based Iterative Reconstruction (aPBIR) for X-ray Computed Tomography - Wu, Meng; Maier, Andreas; Xia, Yan; Fahrig, Rebecca (CT-Meeting 2016 Proceedings (The 4th International Meeting on Image Formation in X-Ray Computed Tomography), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.autotu_1

A Vesselsegmentation-based CycleGAN for Unpaired Multi-modal Retinal Image Synthesis - Sindel, Aline; Maier, Andreas; Christlein, Vincent (Bildverarbeitung für die Medizin 2023, 2023)
Unpaired image-to-image translation of retinal images can efficiently increase the training dataset for deep-learning-based multi-modal retinal registration methods. Our method integrates a vessel segmentation network into the image-to-image translation task by extending the CycleGAN framework. The segmentation network is inserted prior to aUNet vision transformer generator network and serves as a shared representation between both domains. We reformulate the original identity loss to learn the direct mapping between the vessel segmentation and the real image. Additionally, we add a segmentation loss termto ensure shared vessel locations between fake and real images. In the experiments, our method shows a visually realistic look and preserves the vessel structures, which is a prerequisite for generating multi-modal training data for image registration.<br

A Video Guided Solution for Screw Insertion in Orthopedic Plate Fixation - Magaraggia, Jessica; Kleinszig, Gerhard; Graumann, Rainer; Angelopoulou, Elli; Hornegger, Joachim (SPIE Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 2013)
In orthopedic and trauma surgery, metallic plates are used for reduction and fixation of bone fractures. In clinical practice, the intra-operative planning for screw fixation is usually based on fluoroscopic images. Screw fixation is then performed on a free-hand basis. As such, multiple attempts may be required in order to achieve an optimal positioning of the fixing screws. To help the physician insert the screws in accordance to the planned position, we propose a method for screw insertion guidance. Our approach uses a small video camera, rigidly placed on the drill, and a set of small markers that are rigidly fixed on a variable angle drill sleeve. In order to investigate the achievable accuracy of our setup, we simulate the estimation of the drill bit position under two different marker arrangements, planar and 3D, and different noise levels. Furthermore, we motivate our choices for marker design and position given the limited space available for marker positioning, the requirement for accurate position estimation of the drill bit and the illumination changes that could a affect the surgical site. We also describe our proposed marker detection and tracking pipeline. Our simulation results let us conclude that we can achieve an accuracy of 1 ° and 1mm in the estimation of angular orientation and tip position of the drill bit respectively, provided that we have accurate marker detection. © 2013 SPIE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.avideo_5
Keywords: Video-Guided Method; Marker Detection; Orthopedic and Trauma Surgery; Locking Screw Plates

A Visualization Tool for Image Fusion of Artworks - Sindel, Aline; Maier, Andreas; Christlein, Vincent (25th International Conference on Cultural Heritage and New Technologies, 2020)
Keywords: Visualization Tool; Image Registration; Artworks; Prints

Axially Extended-Volume C-Arm CT Using a Reverse Helical Trajectory in the Interventional Room - Yu, Zhicong; Maier, Andreas; Lauritsch, Günter; Vogt, Florian; Schönborn, Manfred; Köhler, Christoph; Hornegger, Joachim; Noo, Frédéric (IEEE Transactions on Medical Imaging, 2015)
UnivIS-Import:2015-03-09:Pub.2015.tech.IMMD.IMMD5.axiall

Background Matters: A Correction Scheme for Dynamic Iterative CBCT with Limited Grid Size - Taubmann, Oliver; Lauritsch, Günter; Krings, Gregor; Maier, Andreas (Proceedings of Fully3D'17, 2017)
UnivIS-Import:2017-11-06:Pub.2017.tech.IMMD.IMMD5.backgr_1

Band-Pass Filter Design by Segmentation in Frequency Domain for Detection of Epithelial Cells in Endomicroscope Images - Bier, Bastian; Mualla, Firas; Steidl, Stefan; Bohr, Christopher; Neumann, Helmut; Maier, Andreas; Hornegger, Joachim (Bildverarbeitung für die Medizin 2015 - Algorithmen, Systeme, Anwendungen, 2015)
Voice hoarseness can have various reasons, one of them is a change of the vocal fold mucus. This change can be examined with micro endoscopes. Cell detection in these images is a difficult task, due to bad image quality, caused by noise and illumination variations. In previous works, it was observed that the repetitive pattern of the cell walls cause an elliptical shape in the Fourier domain [1, 2]. A manual segmentation and back transformation of this shape results in filtered images, where the cell detection is much easier [3]. The goal of this work is to automatically segment the elliptical shape in Fourier domain. Two different approaches are developed to get a suitable band-pass filter: a thresholding and an active contour method. After the band-pass filter is applied, the achieved results are superior to the manual segmentation case.
UnivIS-Import:2018-09-11:Pub.2015.tech.IMMD.IMMD5.bandpa_5
Keywords: medizinische Bildverarbeitung; Zelldetektion

Baptizo: a sensor fusion based model for tracking the identity of human poses - Bazo, Rodrigo; Reis, Eduardo; Adams Seewald, Lucas; Facco Rodrigues, Vinicius; Andre da Costa, Cristiano; Gonzaga, Luiz; Stoffel Antunes, Rodolfo; da Rosa Righi, Rodrigo; Maier, Andreas; Eskofier, Björn; Fahrig, Rebecca; Horz, Tim (Information Fusion, 2020)

Bausteine auf dem Weg zu einer virtuellen Zeitmanschiene – ein Editionsprojekt über lange Dauer - Maier, Andreas; Stromer, Daniel; Christlein, Vincent; Bell, Peter (Magazin für digitale Editionswissenschaften, 2019)
Keywords: European Time Machine; Digital Humanities

Bayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification - Joshaghani, Mohammad; Davari, Amirabbas; Nejati Hatamian, Faezeh; Maier, Andreas; Riess, Christian (IEEE Geoscience and Remote Sensing Letters, 2023)
Hyperspectral remote sensing (HSRS) images have high dimensionality, and labeling HSRS data is expensive and therefore limited to small amounts of pixels. This makes it challenging to use deep neural networks for HSRS image classification. In extreme cases, deep neural networks are even outperformed by traditional models. In this work, we propose to use Bayesian convolutional neural networks (BCNNs) as a potential alternative to convolutional neural networks (CNNs). BCNNs benefit from Bayesian learning, which is more robust against overfitting and inherently provides a measure for uncertainty. We show in experiments on the Pavia Centre, Salinas, and Botswana datasets that a BCNN outperforms a similarly constructed non-Bayesian CNN, an off-the-shelf random forest (RF), and a state-of-the-art Bayesian neural network (BNN). We also show that BCNN is more robust against overfitting compared with the CNN. Furthermore, the BCNN exhibits a remarkably larger capacity for model compression, which makes BCNN a better candidate in hardware-constrained settings. Finally, we show that the BCNN's uncertainty measure can effectively identify misclassified samples. This useful property can be used to detect mislabeled data or to reject predictions with low confidence.
CRIS-Team Scopus Importer:2023-07-21
Keywords: Bayesian learning; convolutional neural networks (CNNs); hyperspectral image classification

Bayesian U-Net for Segmenting Glaciers in Sar Imagery - Hartmann, Andreas; Davari, Amirabbas; Seehaus, Thorsten; Braun, Matthias; Maier, Andreas; Christlein, Vincent (2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021)
Fluctuations of the glacier calving front have an important influence over the ice flow of whole glacier systems. It is therefore important to precisely monitor the position of the calving front. However, the manual delineation of SAR images is a difficult, laborious and subjective task. Convolutional neural networks have previously shown promising results in automating the glacier segmentation in SAR images, making them desirable for further exploration of their possibilities. In this work, we propose to compute uncertainty and use it in an Uncertainty Optimization regime as a novel two-stage process. By using dropout as a random sampling layer in a U-Net architecture, we create a probabilistic Bayesian Neural Network. With several forward passes we create a sampling distribution, which can estimate the model uncertainty for each pixel in the segmentation mask. The additional uncertainty map information can serve as a guideline for the experts in the manual annotation of the data. Furthermore, feeding the uncertainty map to the network leads to 95.24 % Dice similarity, which is an overall improvement in the segmentation performance compared to the state-of-the-art deterministic U-Net-based glacier segmentation pipelines.
Keywords: Image segmentation; Uncertainty; Annotations; Pipelines; Manuals; Probabilistic logic; Radar polarimetry; Glacier segmentation; Bayesian deep learning; Convolutional neural networks; Image Segmentation

Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for AI-assisted medical education and decision making in radiation oncology - Huang, Yixing; Gomaa, Ahmed; Semrau, Sabine; Haderlein, Marlen; Lettmaier, Sebastian; Weissmann, Thomas; Grigo, Johanna; Ben Tkhayat, Hassen; Frey, Benjamin; Gaipl, Udo; Distel, Luitpold; Maier, Andreas; et al. (Frontiers in Oncology, 2023)
Keywords: Large language model; Radiotherapy; Natural language processing; Artificial intelligence; Gray Zone; Clinical decision support (CDS)

Benchmarking Super-Resolution Algorithms on Real Data - Köhler, Thomas; Bätz, Michel; Naderi Boldaji, Farzad; Kaup, André; Maier, Andreas; Riess, Christian (2017)
Over the past decades, various super-resolution (SR) techniques have been developed to enhance the spatial resolution of digital images. Despite the great number of methodical contributions, there is still a lack of comparative validations of SR under practical conditions, as capturing real ground truth data is a challenging task. Therefore, current studies are either evaluated 1) on simulated data or 2) on real data without a pixel-wise ground truth. To facilitate comprehensive studies, this paper introduces the publicly available Super-Resolution Erlangen (SupER) database that includes real low-resolution images along with high-resolution ground truth data. Our database comprises image sequences with more than 20k images captured from 14 scenes under various types of motions and photometric conditions. The datasets cover four spatial resolution levels using camera hardware binning. With this database, we benchmark 15 single-image and multi-frame SR algorithms. Our experiments quantitatively analyze SR accuracy and robustness under realistic conditions including independent object and camera motion or photometric variation

Beyond Human Forgeries: An Investigation into Detecting Diffusion-Generated Handwriting - Carriere, Guillaume; Nikolaidou, Konstantina; Kordon, Florian Johannes; Mayr, Martin; Seuret, Mathias; Christlein, Vincent (Document Analysis and Recognition – ICDAR 2023 Workshops, 2023)
Methods for detecting forged handwriting are usually based on the assumption that the forged handwriting is produced by humans. Authentic-looking handwriting, however, can also be produced synthetically. Diffusion-based generative models have recently gained popularity as they produce striking natural images and are also able to realistically mimic a person’s handwriting. It is, therefore, reasonable to assume that these models will be used to forge handwriting in the near future, adding a new layer to handwriting forgery detection. We show for the first time that the identification of synthetic handwritten data is possible by a small Convolutional Neural Network (ResNet18) reaching accuracies of 90%. We further investigate the existence of distinct discriminative features in synthetic handwriting data produced by latent diffusion models that could be exploited to build stronger detection methods. Our experiments indicate that the strongest discriminative features do not come from generation artifacts, letter shapes, or the generative model’s architecture, but instead originate from real-world artifacts in genuine handwriting that are not reproduced by generative methods.<br
CRIS-Team Scopus Importer:2023-10-13
Keywords: Forensics Analysis; Diffusion Models; Handwriting Generation; Synthetic Image Generation; Forgery Detection; Biometrics

Bifurcation matching for consistent cerebral vessel labeling in CTA of stroke patients - Rist, Leonhard; Taubmann, Oliver; Thamm, Florian; Ditt, Hendrik; Sühling, Michael; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2022)
Purpose Vessel labeling is a prerequisite for comparing cerebral vasculature across patients, e.g., for straightened vessel examination or for localization. Extracting vessels from computed tomography angiography scans may come with a trade-off in segmentation accuracy. Vessels might be neglected or artificially created, increasing the difficulty of labeling. Related work mainly focuses on magnetic resonance angiography without stroke and uses trainable approaches requiring costly labels. Methods We present a robust method to identify major arteries and bifurcations in cerebrovascular models generated from existing segmentations. To localize bifurcations of the Circle of Willis, candidate paths for the adjacent vessels of interest are identified using registered landmarks. From those paths, the optimal ones are extracted by recursively maximizing an objective function for all adjacent vessels starting from a bifurcation to avoid erroneous paths and compensate for stroke. Results In 100 CTA stroke data sets for evaluation, 6 bifurcation locations are placed correctly in 85% of cases; 92.5% when allowing a margin of 5 mm. On average, 14 vessels of interest are found in 90% of the cases and traced correctly end-to-end in 73.5%. The baseline achieves similar detection rates but only 35.5% of the arteries are traced in full. Conclusion Formulating the vessel labeling process as a maximization task for bifurcation matching can vastly improve accurate vessel tracing. The proposed algorithm only uses simple features and does not require expensive training dat

Bildanalyse - Maier, Andreas; Christlein, Vincent; Breininger, Katharina; Wuerfl, Tobias; Unberath, Mathias; Rieß, Christian (Handbuch der Künstichen Intelligenz, 2020)

Binarization Driven Blind Deconvolution for Document Image Restoration - Köhler, Thomas; Maier, Andreas; Christlein, Vincent (Pattern Recognition, 2015)
UnivIS-Import:2015-11-17:Pub.2015.tech.IMMD.IMMD5.binari_4

Blood glucose level prediction based on support vector regression using mobile platforms - Reymann, Maximilian; Dorschky, Eva; Groh, Benjamin; Martindale, Christine; Blank, Peter; Eskofier, Björn (38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016)
The correct treatment of diabetes is vital to a patient’s health: Staying within defined blood glucose levels prevents dangerous short- and long-term effects on the body. Mobile devices informing patients about their future blood glucose levels could enable them to take counter-measures to prevent hypo or hyper periods. Previous work addressed this challenge by predicting the blood glucose levels using regression models. However, these approaches required a physiological model, representing the human body’s response to insulin and glucose intake, or are not directly applicable to mobile platforms (smart phones, tablets). In this paper, we propose an algorithm for mobile platforms to predict blood glucose levels without the need for a physiological model. Using an online software simulator program, we trained a Support Vector Regression (SVR) model and exported the parameter settings to our mobile platform. The prediction accuracy of our mobile platform was evaluated with pre-recorded data of a type 1 diabetes patient. The blood glucose level was predicted with an error of 19 % compared to the true value. Considering the permitted error of commercially used devices of 15 %, our algorithm is the basis for further development of mobile prediction algorithms.

Boosting of Prosodic and Pronunciation Features to Detect Mispronunciations of Non-Native Children - Hacker, Christian; Cincarek, Tobias; Maier, Andreas; Heßler, Andre; Nöth, Elmar (ICASSP, 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings, 2007)

BOSS: Bones, organs and skin shape model - Shetty, Karthik; Birkhold, Annette; Jaganathan, Srikrishna; Strobel, Norbert; Egger, Bernhard; Kowarschik, Markus; Maier, Andreas (Computers in Biology and Medicine, 2023)
A virtual anatomical model of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions. For these tasks, it is highly desirable that the patient's surface and internal organs are of high quality for any pose and shape estimate. At present, the majority of statistical shape models (SSMs) are restricted to a small number of organs or bones or do not adequately represent the general population. To address this, we propose a deformable human shape and pose model that combines skin, internal organs, and bones, learned from CT images. By modeling the statistical variations in a pose-normalized space using probabilistic PCA while also preserving joint kinematics, our approach offers a holistic representation of the body that can be beneficial for automation in various medical applications. In an interventional setup, our model could, for example, facilitate automatic system/patient positioning, organ-specific iso-centering, automated collimation or collision prediction. We assessed our model's performance on a registered dataset, utilizing the unified shape space, and noted an average error of 3.6 mm for bones and 8.8 mm for organs. By utilizing solely skin surface data or patient metadata like height and weight, we find that the overall combined error for bone-organ measurement is 8.68 mm and 8.11 mm, respectively. To further verify our findings, we conducted additional tests on publicly available datasets with multi-part segmentations, which confirmed the effectiveness of our model. In the diverse TotalSegmentator dataset, the errors for bones and organs are observed to be 5.10mm and 8.72mm, respectively. Our work shows that anatomically parameterized statistical shape models can be created accurately and in a computationally efficient manner. The proposed approach enables the construction of shape models that can be directly integrated into to various medical applications.
CRIS-Team Scopus Importer:2023-09-08
Keywords: Forward kinematics; Human body; Mesh; Shape model

Brain Parenchyma and Vessel Separation in 3D Digital Subtraction Angiography Images - Endres, Jürgen; Rohkohl, Christopher; Royalty, Kevin; Schafer, Sebastian; Maier, Andreas; Dörfler, Arnd; Kowarschik, Markus (Bildverarbeitung für die Medizin: Algorithmen-Systeme-Anwendungen., 2017)
UnivIS-Import:2017-03-24:Pub.2017.tech.IMMD.IMMD5.brainp_0

Breast density assessment using wavelet features on mammograms - Schebesch, Frank; Unberath, Mathias; Andersen, Ingwer; Maier, Andreas (Informatik aktuell, 2017)
Breast density differs from almost entirely fatty to extremely dense tissue composition. In mammography screenings, physicians are often supported by computer-aided detection and diagnosis systems (CAD) whose detection rate is affected by the density of the breast. An automatic pre-assessment of breast density would enable a specific analysis adapted to each density class. Digital mammograms from the INbreast database [1] are decomposed into Haar-Wavelet components and several levels are used for classification. A random forest classifier is applied on the averaged Wavelet components for four class densities which yields an accuracy of 64.53% in CC-view and 51.22% in MLO-view. The 3-class problem with a combined class of medium densities yields an accuracy of 73.89% in CC-view and 67.80% in MLO-view.
CRIS-Team Scopus Importer:2022-06-05

Bridge to Real Data: Empirical Multiple Material Calibration for Learning-Based Material Decomposition - Lu, Yanye; Berger, Martin; Manhart, Michael; Choi, Jang-Hwan; Hoheisel, Martin; Kowarschik, Markus; Fahrig, Rebecca; Ren, Qiushi; Hornegger, Joachim; Maier, Andreas (Proceedings of the 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016)
In this study, we proposed an empirical multi-material calibration pipeline for learning-based material decomposition. We used realistic short scan CT data from a general metric phantom using a Siemens C-arm system, and built the corresponding numeric phantom data in a software framework. After that we applied registration approaches for matching the simulated data to the acquired data, which generates prior knowledge for the following material decomposition process, as well as the ground truth for quantitative evaluations. According to the preliminary decomposition results, we successfully decomposed the inserted phantom plugs of different materials using learning-based material decomposition process, which indicates that the proposed approach is valid for learning-based material decomposition.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.bridge_9

Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data - Köhler, Thomas; Bätz, Michel; Naderi Boldaji, Farzad; Kaup, André; Maier, Andreas; Riess, Christian (2018)

Browsing Through Closed Books: Evaluation of Pre-processing Methods for Page Extraction of a 3-D CT Book Volume - Stromer, Daniel; Christlein, Vincent; Schön, Tobias; Holub, Wolfgang; Maier, Andreas (Proceedings of the 2nd International Conference on Advanced Materials Research and Manufacturing Technologies, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.browsi_0

Browsing Through Closed Books: Fully Automatic Book Page Extraction from a 3-D X-ray CT Volume - Stromer, Daniel; Christlein, Vincent; Schön, Tobias; Holub, Wolfgang; Maier, Andreas (The 14th IAPR International Conference on Document Analysis and Recognition, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.browsi_66

Browsing through sealed historical manuscripts by using 3-D computed tomography with low-brilliance X-ray sources - Stromer, Daniel; Christlein, Vincent; Martindale, Christine; Zippert, Patrick; Haltenberger, Eric; Hausotte, Tino; Maier, Andreas (Scientific Reports, 2018)
Severely damaged historical documents are extremely fragile. In many cases, their secrets remain concealed beneath their cover. Recently, non-invasive digitization approaches based on 3-D scanning have demonstrated the ability to recover single pages or letters without the need to open the manuscripts. This can even be achieved using conventional micro-CTs without the need for synchrotron hardware. However, not all manuscripts may be suited for such techniques due to their material and X-ray properties. In order to recommend which manuscripts and which inks are best suited for such a process, we investigate six inks that were commonly used in ancient times: malachite, three types of iron gall, Tyrian purple, and buckthorn. Image contrast is explored over the complete pipeline, from the X-ray CT scan and page extraction to the virtual flattening of the page image. We demonstrate, that all inks containing metallic particles are visible in the output, a decrease of the X-ray energy enhances the readability, and that the visibility highly depends on the X-ray attenuation of the ink’s metallic ingredients and their concentration. Based on these observations, we give recommendations on how to select the appropriate imaging parameters.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.browsi

Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection - Thamm, Florian; Taubmann, Oliver; Jürgens, Markus; Thamm, Aleksandra; Denzinger, Felix; Rist, Leonhard; Ditt, Hendrik; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022)
Ischemic strokes are often caused by large vessel occlusions (LVOs), which can be visualized and diagnosed with Computed Tomography Angiography scans. As time is brain, a fast, accurate and automated diagnosis of these scans is desirable. Human readers compare the left and right hemispheres in their assessment of strokes. A large training data set is required for a standard deep learning-based model to learn this strategy from data. As labeled medical data in this field is rare, other approaches need to be developed. To both include the prior knowledge of side comparison and increase the amount of training data, we propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres or hemisphere subregions from different patients. The subregions cover vessels commonly affected by LVOs, namely the internal carotid artery (ICA) and middle cerebral artery (MCA). In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres. Furthermore, we propose an extension of that architecture to process the individual hemisphere subregions. All configurations predict the presence of an LVO, its side, and the affected subregion. We show the effect of recombination as an augmentation strategy in a 5-fold cross validated ablation study. We enhanced the AUC for patient-wise classification regarding the presence of an LVO of all investigated architectures. For one variant, the proposed method improved the AUC from 0.73 without augmentation to 0.89. The best configuration detects LVOs with an AUC of 0.91, LVOs in the ICA with an AUC of 0.96, and in the MCA with 0.91 while accurately predicting the affected side.<br
Keywords: Computed Tomography Angiography; Stroke; Augmentation

CAD-RADS Scoring using Deep Learning and Task-Specific Centerline Labeling - Denzinger, Felix; Wels, Michael; Taubmann, Oliver; Gülsün, Mehmet A.; Schöbinger, Max; Andre, Florian; Buss, Sebastian J.; Goerich, Johannes; Sühling, Michael; Maier, Andreas; Breininger, Katharina (Medical Imaging with Deep Learning, 2022)

CAD-RADS Scoring using Deep Learning and Task-Specific Centerline Labeling - Denzinger, Felix; Wels, Michael; Taubmann, Oliver; Gülsün, Mehmet A.; Schöbinger, Max; Andre, Florian; Buss, Sebastian J.; Goerich, Johannes; Sühling, Michael; Maier, Andreas; Breininger, Katharina (PMLR, 2022)
With coronary artery disease (CAD) persisting to be one of the leading causes of death worldwide, interest in supporting physicians with algorithms to speed up and improve diagnosis is high. In clinical practice, the severeness of CAD is often assessed with a coronary CT angiography (CCTA) scan and manually graded with the CAD-Reporting and Data System (CAD-RADS) score. The clinical questions this score assesses are whether patients have CAD or not (rule-out) and whether they have severe CAD or not (hold-out). In this work, we reach new state-of-the-art performance for automatic CAD-RADS scoring. We propose using severity-based label encoding, test time augmentation (TTA) and model ensembling for a task-specific deep learning architecture. Furthermore, we introduce a novel task-and model-specific, heuristic coronary segment labeling, which subdivides coronary trees into consistent parts across patients. It is fast, robust, and easy to implement. We were able to raise the previously reported area under the receiver operating characteristic curve (AUC) from 0.914 to 0.942 in the rule-out and from 0.921 to 0.950 in the hold-out task respectively.<br

CaFFe - a Benchmark Dataset for Glacier Calving Front Extraction from Synthetic Aperture Radar Imagery - Gourmelon, Nora; Seehaus, Thorsten; Klink, Julian; Braun, Matthias; Maier, Andreas; Christlein, Vincent (IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023)

Calibration by differentiation – Self-supervised calibration for X-ray microscopy using a differentiable cone-beam reconstruction operator - Thies, Mareike; Wagner, Fabian; Huang, Yixing; Gu, Mingxuan; Kling, Lasse; Pechmann, Sabrina; Aust, Oliver; Grüneboom, Anika; Schett, Georg; Christiansen, Silke H.; Maier, Andreas (Journal of Microscopy, 2022)
High-resolution X-ray microscopy (XRM) is gaining interest for biological investigations of extremely small-scale structures. XRM imaging of bones in living mice could provide new insights into the emergence and treatment of osteoporosis by observing osteocyte lacunae, which are holes in the bone of few micrometers in size. Imaging living animals at that resolution, however, is extremely challenging and requires very sophisticated data processing converting the raw XRM detector output into reconstructed images. This paper presents an open-source, differentiable reconstruction pipeline for XRM data which analytically computes the final image from the raw measurements. In contrast to most proprietary reconstruction software, it offers the user full control over each processing step and, additionally, makes the entire pipeline deep learning compatible by ensuring differentiability. This allows fitting trainable modules both before and after the actual reconstruction step in a purely data-driven way using the gradient-based optimizers of common deep learning frameworks. The value of such differentiability is demonstrated by calibrating the parameters of a simple cupping correction module operating on the raw projection images using only a self-supervisory quality metric based on the reconstructed volume and no further calibration measurements. The retrospective calibration directly improves image quality as it avoids cupping artifacts and decreases the difference in gray values between outer and inner bone by 68% to 94%. Furthermore, it makes the reconstruction process entirely independent of the XRM manufacturer and paves the way to explore modern deep learning reconstruction methods for arbitrary XRM and, potentially, other flat-panel CT systems. This exemplifies how differentiable reconstruction can be leveraged in the context of XRM and, hence, is an important step toward the goal of reducing the resolution limit of in-vivo bone imaging to the single micrometer domain.
Keywords: X-ray microscopy; computed tomography; inverse problems; reconstruction; deep learning; known operator learning

Calibration-free beam hardening reduction in x-ray CBCT using the epipolar consistency condition and physical constraints. - Würfl, Tobias; Hoffmann, Mathis; Aichert, André; Maass, Nicole; Dennerlein, Frank; Maier, Andreas (Medical Physics, 2019)
The beam hardening effect is a typical source of artifacts in x-ray cone beam computed tomography (CBCT). It causes streaks in reconstructions and corrupted Hounsfield units toward the center of objects, widely known as cupping artifacts. We present a novel efficient projection data-based method for reduction of beam-hardening artifacts and incorporate physical constraints on the shape of the compensation functions. The method is calibration-free and requires no additional knowledge of the scanning setup. The mathematical model of the beam hardening effect caused by a single material is analyzed. We show that the effect of beam hardening on the resulting functions on the line integral measurements are monotonous and concave functions of the ideal data. This holds irrespective of any limiting assumptions on the energy dependency of the material, the detector response or properties of the x-ray source. A regression model for the beam hardening effect respecting these theoretical restrictions is proposed. Subsequently, we present an efficient method to estimate the parameters of this model directly in projection domain using an epipolar consistency condition. Computational efficiency is achieved by exploiting the linearity of an intermediate function in the formulation of our optimization problem. algorithm is effective even in challenging measured data scenarios with additional sources of inconsistency. The combination of mathematical consistency condition and a compensation model that is based on the properties of x-ray physics enables us to improve image quality of measured data retrospectively and to decrease the need for calibration in a data-driven manner. BACKGROUND PURPOSE METHOD RESULTS CONCLUSIONS

Calibration of a Camera-Based Guidance Solution for Orthopedic and Trauma Surgery - Magaraggia, Jessica; Egli, Adrian; Kleinszig, Gerhard; Graumann, Rainer; Angelopoulou, Elli; Hornegger, Joachim (Bildverarbeitung für die Medizin 2013, 2013)
In orthopedic and trauma surgery, fracture reduction usually requires the use of metallic plates and their fixation by means of screws. The employment of guidance solutions during surgical procedures has become of great importance during the last decades. Our guidance solution exploits a small video camera placed directly on the instrument, for example a drill, and a set of small markers placed around the location where the drilling needs to be performed. A calibration step is required in order to determine the relative position of the instrument tip and axis w.r.t the coordinate system of the video camera. In this paper we describe a calibration method for our guidance solution. This calibration method exploits optical markers and a calibration plate whose geometry is known. Moreover, we show how we can exploit directly the image acquired by the video camera during the calibration in order to define an error measure to estimate the accuracy of the calibration. With this method, we achieved respectively an accuracy of 0.23mm and 3.40° in the estimation of the instrument tip position and of the orientation of the instrument axis.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.calibr_8
Keywords: Instrument calibration; Orthopedic surgery; Image-guidance

Caller: Computer Assisted Language Learning from Erlangen - Pronunciation Training and More - Hacker, Christian; Maier, Andreas; Heßler, Andre; Guthunz, Ute; Nöth, Elmar (Proc. Int. Conf. Interactive Computer Aided Learning (ICL), 2007)

Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery - Gourmelon, Nora; Seehaus, Thorsten; Braun, Matthias; Maier, Andreas; Christlein, Vincent (Earth System Science Data, 2022)
Exact information on the calving front positions of marine- or lake-terminating glaciers is a fundamental glacier variable for analyzing ongoing glacier change processes and assessing other variables like frontal ablation rates. In recent years, researchers started implementing algorithms that can automatically detect the calving fronts on satellite imagery. Most studies use optical images, as calving fronts are often easy to distinguish in these images due to the sufficient spatial resolution and the presence of different spectral bands, allowing the separation of ice features. However, detecting calving fronts on synthetic aperture radar (SAR) images is highly desirable, as SAR images can also be acquired during the polar night and are independent of weather conditions (e.g., cloud cover), facilitating year-round monitoring worldwide. In this paper, we present a benchmark dataset (Gourmelon et al., 2022b) of SAR images from multiple regions of the globe with corresponding manually defined labels providing information on the position of the calving front (https://doi.org/10.1594/PANGAEA.940950). With this dataset, different approaches for the detection of glacier calving fronts can be implemented, tested, and their performance fairly compared so that the most effective approach can be determined. The dataset consists of 681 samples, making it large enough to train deep learning segmentation models. It is the first dataset to provide long-term glacier calving front information from multi-mission data. As the dataset includes glaciers from Antarctica, Greenland, and Alaska, the wide applicability of models trained and tested on this dataset is ensured. The test set is independent of the training set so that the generalization capabilities of the models can be evaluated. We provide two sets of labels: one binary segmentation label to discern the calving front from the background, and one label for multi-class segmentation of different landscape classes. Unlike other calving front datasets, the presented dataset contains not only the labels but also the corresponding preprocessed and geo-referenced SAR images as PNG files. The ease of access to the dataset will allow scientists from other fields, such as data science, to contribute their expertise. With this benchmark dataset, we enable comparability between different front detection algorithms and improve the reproducibility of front detection studies. Moreover, we present one baseline model for each kind of label type. Both models are based on the U-Net, one of the most popular deep learning segmentation architectures. In the following two post-processing procedures, the segmentation results are converted into 1-pixel-wide front delineations. By providing both types of labels, both approaches can be used to address the problem. To assess the performance of different models, we suggest first reviewing the segmentation results using the recall, precision, F1 score, and the Jaccard index. Second, the front delineation can be evaluated by calculating the mean distance error to the labeled front. The presented vanilla models provide a baseline of 150 m ± 24 m mean distance error for the Mapple Glacier in Antarctica and 840 m ± 84 m for the Columbia Glacier in Alaska, which has a more complex calving front, consisting of multiple sections, compared with a laterally well constrained, single calving front of Mapple Glacie
Keywords: Deep Learning; Calving Front; Dataset; Computer Vision

Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge - Zhuang, Xiahai; Xu, Jiahang; Luo, Xinzhe; Chen, Chen; Ouyang, Cheng; Rueckert, Daniel; Campello, Victor M.; Lekadir, Karim; Vesal, Sulaiman; Ravikumar, Nishant; Liu, Yashu; Luo, Gongning; et al. (Medical Image Analysis, 2022)
Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, compared with the other sequences LGE CMR images with gold standard labels are particularly limited. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation focusing on myocardial wall of the left ventricle and blood cavity of the two ventricles. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the ventricle segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised domain adaptation (UDA) methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and UDA methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challenge continues as an ongoing resource, and the gold standard segmentation as well as the MS-CMR images of both the training and test data are available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/).
CRIS-Team Scopus Importer:2022-08-12
Keywords: Benchmark; Cardiac MRI segmentation; Challenge; Multi-sequence

C-arm CT imaging using the extended line-ellipse-line trajectory: first implementation and initial results - Guo, Zijia (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2018)
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.carmct

C-arm CT imaging using the extended line-ellipse-line trajectory: Seamless FBP reconstruction from real data - Guo, Zijia; Lauritsch, Günter; Maier, Andreas; Noo, Frédéric (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2020)
We present further progress on the implementation of C-arm CT imaging with the extended line-ellipse-line (LEL) trajectory. This novel data acquisition geometry is designed to enhance image quality in interventional radiology. Previously, we showed that robust extended LEL data acquisition is feasible using a state-of-the-art multi-axis robotic C-arm (ARTIS pheno, Siemens Healthcare GmbH, Germany) and we also showed that accurate reconstruction from real data can be obtained using an iterative algorithm. The extensive computational effort required by such an algorithm is however not suitable for clinical translation. Reconstruction using a filtered-backprojection (FBP) formula would be practical. To use such a formula, there needs to be a technique to handle imperfections in the data acquisition geometry, which result from mechanical vibrations and gravity effects. We recently presented such a technique, but this development was only carried out for a single cycle of the LEL trajectory. In this work, we address the more challenging issue of reconstructing the volume covered by multiple cycles of the trajectory. Specifically, we propose an extension of our single cycle approach to multiple cycles. We successfully demonstrate that our procedure now allows seamless volume reconstruction from real data using a cone-beam performance phantom as well as an anthropomorphic head phantom. Our results bring the extended LEL trajectory closer to clinical deployment for improved image quality in interventional radiology. Further work will focus on increasing the number of views to avoid few view artifacts and on thoroughly demonstrating image quality benefits.
CRIS-Team Scopus Importer:2020-06-30

C-arm CT imaging with the extended line-ellipse-line trajectory: First implementation on a state-of-the-art robotic angiography system - Guo, Zijia; Lauritsch, Guenter; Maier, Andreas; Kugler, Patrick; Islam, Mohammad; Vogt, Florian; Noo, Frederic (Physics in Medicine and Biology, 2020)
Three-dimensional cone-beam imaging has become valuable in interventional radiology. Currently, this tool, referred to as C-arm CT, employs a circular short-scan for data acquisition, which limits the axial volume coverage and yields unavoidable cone-beam artifacts. To improve flexibility in axial coverage and image quality, there is a critical need for novel data acquisition geometries and related image reconstruction algorithms. For this purpose, we previously introduced the extended line-ellipse-line trajectory, which allows complete scanning of arbitrary volume lengths in the axial direction together with adjustable axial beam collimation, from narrow to wide depending on the targeted application. A first implementation of this trajectory on a state-of-the-art robotic angiography system is reported here. More specifically, an assessment of the quality of this first implementation is presented. The assessment is in terms of geometric fidelity and repeatability, complemented with a first visual inspection of how well the implementation enables imaging an anthropomorphic head phantom. The geometric fidelity analysis shows that the ideal trajectory is closely emulated, with only minor deviations that have no impact on data completeness and clinical practicality. Also, mean backprojection errors over short-term repetitions are shown to be below the detector pixel size at field-of-view center for most views, which indicates repeatability is satisfactory for clinical utilization. These repeatability observations are further supported by values of the Structural Similarity Index Metric above 94% for reconstructions of the FORBILD head phantom from computer-simulated data based on repeated data acquisition geometries. Last, the real data experiment with the anthropomorphic head phantom shows that the high contrast features of the phantom are well reconstructed without distortions as well as without breaks or other disturbing transition zones, which was not obvious given the complexity of the data acquisition geometry and the major variations in axial coverage that occur over the scan.
CRIS-Team Scopus Importer:2020-10-16
Keywords: C-arm CT; cone-beam; data acquisition; head imaging; implementation; reconstruction; trajectory

Catheter artifact reduction (CAR) in dynamic cardiac chamber imaging with interventional C-arm CT - Müller, Kerstin; Lauritsch, Günter; Schwemmer, Chris; Maier, Andreas; Taubmann, Oliver; Abt, Bernd; Köhler, Henning; Nöttling, Alois; 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.cathet_3

Cavity Segmentation in X-ray Microscopy Scans of Mouse Tibiae - Gu, Mingxuan; Thies, Mareike; Wagner, Fabian; Pechmann, Sabrina; Aust, Oliver; Weidner, Daniela; Neag, Georgiana; Pan, Zhaoya; Utz, Jonas; Schett, Georg; Christiansen, Silke; Uderhardt, Stefan; et al. (Informatik aktuell, 2023)
Osteoporosis is a chronic disease that causes lower bone density and makes bones fragile. This severely impairs patient life qualities and increases the burden on the social and health care system. X-ray microscopy (XRM) allows tracking of osteoporosis-related changes at a microstructural level in the bone, entailing the characterization of osteocyte lacunae and blood vessel canals. Unfortunately, no segmentation methods for micro-structures in XRM images have yet been established. In this work, we compare the performance of a traditional thresholding-based method with three deep learning networks including 2D and 3D models in both binary and multi-class segmentation. We further propose a clustering method to automatically distinguish blood vessels from lacunae for the binary methods. The performance is evaluated with Dice score (F1 score). The thresholding-based method reaches a mean Dice score of 0.729, which the deep learning models improve by 0.129 - 0.168.
CRIS-Team Scopus Importer:2023-11-03

Cell Detection for Asthma on Partially Annotated Whole Slide Images: Learning to be EXACT - Marzahl, Christian; Bertram, Christof A.; Wilm, Frauke; Voigt, Jörn; Barton, Ann K.; Klopfleisch, Robert; Breininger, Katharina; Maier, Andreas; Aubreville, Marc (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems - Huang, Yixing; Fan, Fuxin; Syben-Leisner, Christopher; Roser, Philipp; Mill, Leonid; Maier, Andreas (Medical Image Analysis, 2021)
Due to the lack of a standardized 3D cephalometric analysis methodology, 2D cephalograms synthesized from 3D cone-beam computed tomography (CBCT) volumes are widely used for cephalometric analysis in dental CBCT systems. However, compared with conventional X-ray film based cephalograms, such synthetic cephalograms lack image contrast and resolution, which impairs cephalometric landmark identification. In addition, the increased radiation dose applied to acquire the scan for 3D reconstruction causes potential health risks. In this work, we propose a sigmoid-based intensity transform that uses the nonlinear optical property of X-ray films to increase image contrast of synthetic cephalograms from 3D volumes. To improve image resolution, super resolution deep learning techniques are investigated. For low dose purpose, the pixel-to-pixel generative adversarial network (pix2pixGAN) is proposed for 2D cephalogram synthesis directly from two cone-beam projections. For landmark detection in the synthetic cephalograms, an efficient automatic landmark detection method using the combination of LeNet-5 and ResNet50 is proposed. Our experiments demonstrate the efficacy of pix2pixGAN in 2D cephalogram synthesis, achieving an average peak signal-to-noise ratio (PSNR) value of 33.8 with reference to the cephalograms synthesized from 3D CBCT volumes. Pix2pixGAN also achieves the best performance in super resolution, achieving an average PSNR value of 32.5 without the introduction of checkerboard or jagging artifacts. Our proposed automatic landmark detection method achieves 86.7% successful detection rate in the 2 mm clinical acceptable range on the ISBI Test1 data, which is comparable to the state-of-the-art methods. The method trained on conventional cephalograms can be directly applied to landmark detection in the synthetic cephalograms, achieving 93.0% and 80.7% successful detection rate in 4 mm precision range for synthetic cephalograms from 3D volumes and 2D projections, respectively.<br
Keywords: Deep learning; cephalogram synthesis; super resolution; landmark detection; perspective deformation learning

Cerebral Vessel Tree Estimation from Non-contrast CT using Deep Learning Methods - Schauer, Jonas; Thamm, Florian; Taubmann, Oliver; Maier, Andreas (Bildverarbeitung für die Medizin 2023, 2023)
Non-contrast computed tomography (NCCT) is the primary first-line neuroimaging technique in the clinical workflow for patients with suspected ischemic stroke. We present a deep learning model to estimate the cerebral vessel tree from the NCCT instead of subsequently performed contrast-enhanced imaging techniques, e.g. computed tomography angiography (CTA). We employ a volumetric sliding window approach and feed the patches to a 3D U-Net. This U-Net has two outputs, a probability map that indicates vessel presence and a prediction of the corresponding CTA patch. The CTA regression target is used in addition to the supervised segmentation to optimize the 3D U-Net in a GAN-like manner in order to generate more realistic estimations for the vessel tree. Comparing our proposed model with the current state of the art for this task, a 2D U-Net operating on axial NCCT slices, we were able to slightly increase quantitative overlap metrics as well as achieve notably improved qualitative results w.r.t. spatial continuity of the segmented vessel tree.<br

ChainLineNet: Deep-Learning-Based Segmentation and Parameterization of Chain Lines in Historical Prints - Sindel, Aline; Klinke, Thomas; Maier, Andreas; Christlein, Vincent (Journal of Imaging, 2021)
The paper structure of historical prints is sort of a unique fingerprint. Paper with the same origin shows similar chain line distances. As the manual measurement of chain line distances is time consuming, the automatic detection of chain lines is beneficial. We propose an end-to-end trainable deep learning method for segmentation and parameterization of chain lines in transmitted light images of German prints from the 16th Century. We trained a conditional generative adversarial network with a multitask loss for line segmentation and line parameterization. We formulated a fully differentiable pipeline for line coordinates’ estimation that consists of line segmentation, horizontal line alignment, and 2D Fourier filtering of line segments, line region proposals, and differentiable line fitting. We created a dataset of high-resolution transmitted light images of historical prints with manual line coordinate annotations. Our method shows superior qualitative and quantitative chain line detection results with high accuracy and reliability on our historical dataset in comparison to competing methods. Further, we demonstrated that our method achieves a low error of less than 0.7 mm in comparison to manually measured chain line distances.<br
Keywords: line segmentation; line detection; line parameterization; generative adversarial networks; Fourier transform; differentiable line fitting; chain lines; paper structure; historical prints

Choroidal Neovascularization Analyzed on Ultra-High Speed Swept Source Optical Coherence Tomography Angiography Compared to Spectral Domain Optical Coherence Tomography Angiography - Louzada, Ricardo N.; Novais, Eduardo A.; Adhi, Mehreen; Cole, Emily D.; Moult, Eric M.; Husvogt, Lennart; Witkin, André J.; Baumal, Caroline R.; Fujimoto, James G.; Duker, Jay S.; Waheed, Nadia K. (Investigative Ophthalmology & Visual Science, 2016)
Purpose : Cross-sectional, observational study to compare visualization of choroidal neovascularization (CNV) secondary to age-related macular degeneration (AMD) using an ultra-high speed swept-source (SS)-optical coherence tomography angiography (OCTA) prototype versus a spectral-domain (SD)-OCTA device. Methods : Patients were imaged on SD-OCT and SS-OCT devices on the same day. The SD-OCT device employed is the RTVue Avanti that operates at 840nm wavelength and 70,000 A-scans/second. The SS-OCT device used is an ultra-high speed long-wavelength prototype that operates at 1050nm wavelength and 400,000 A-scans/second. Two observers independently measured the CNV area on OCTA en face images from the two devices using ImageJ. The non-parametric Wilcoxon signed-rank test was used to compare area measurements. Results : Fourteen eyes from 13 patients were enrolled. The CNV in 11 eyes (78.6%) were classified as type-1, 2 eyes (14.3%) as type-2, and 1 eye (7.1%) as mixed type. The mean CNV areas measured using SS-OCT and SD-OCT 3mm x 3mm OCTA were 0.949 ± 1.168mm2 and 0.340 ± 0.301mm2, respectively (p=0.001). For the 6mm x 6mm OCTA the CNV areas using SS-OCT and SD-OCT were 1.218 ± 1.284mm2 and 0.604 ± 0.597mm2, respectively (p=0.0019). The field of view did not significantly affect the measured CNV area (p=0.19 and p=0.18 for SS-OCT and SD-OCT respectively). Conclusions : SS-OCTA measurements yielded significantly larger CNV areas than SD-OCTA. It is possible that SS-OCTA is better able to demarcate the full extent of CNV vasculatur
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.choroi_2
Keywords: oct; octa; oct angiography; cnv; choroidal neovascularization; ss-oct; sd-oct

Classification at the accuracy limit: facing the problem of data ambiguity - Metzner, Claus; Schilling, Achim; Traxdorf, Maximilian; Tziridis, Konstantin; Maier, Andreas; Schulze, Holger; Krauß, Patrick (Scientific Reports, 2022)
Data classification, the process of analyzing data and organizing it into categories or clusters, is a fundamental computing task of natural and artificial information processing systems. Both supervised classification and unsupervised clustering work best when the input vectors are distributed over the data space in a highly non-uniform way. These tasks become however challenging in weakly structured data sets, where a significant fraction of data points is located in between the regions of high point density. We derive the theoretical limit for classification accuracy that arises from this overlap of data categories. By using a surrogate data generation model with adjustable statistical properties, we show that sufficiently powerful classifiers based on completely different principles, such as perceptrons and Bayesian models, all perform at this universal accuracy limit under ideal training conditions. Remarkably, the accuracy limit is not affected by certain non-linear transformations of the data, even if these transformations are non-reversible and drastically reduce the information content of the input data. We further compare the data embeddings that emerge by supervised and unsupervised training, using the MNIST data set and human EEG recordings during sleep. We find for MNIST that categories are significantly separated not only after supervised training with back-propagation, but also after unsupervised dimensionality reduction. A qualitatively similar cluster enhancement by unsupervised compression is observed for the EEG sleep data, but with a very small overall degree of cluster separation. We conclude that the handwritten letters in MNIST can be considered as ’natural kinds’, whereas EEG sleep recordings are a relatively weakly structured data set, so that unsupervised clustering will not necessarily re-cover the human-defined sleep stages.
CRIS-Team Scopus Importer:2023-03-07

Classification of Body Regions Based on MRI Log Files - Kuhnert, Nadine; Lindenmayr, Oliver; Maier, Andreas (Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017, 2018)
UnivIS-Import:2017-11-06:Pub.2018.tech.IMMD.IMMD5.classi
Keywords: Classification; Data Mining; Log File Analysis; System Usage

Classification of body regions based on MRI log files - Kuhnert, Nadine; Lindenmayr, Oliver; Maier, Andreas (Proceedings of the 10th International Conference on Computer Recognition Systems, CORES 2017, 2018)
Every Siemens Magnetic Resonance Imaging (MRI) system consistently writes events into log files while the system is running. The log files and their contents are constantly refined by software developers. This results in different information contents depending on the software version. One information that is missing in some log files is the examined body region. As the body region is crucial for usage analysis, we used pattern recognition methods to estimate the examined body region for software versions not logging it automatically. We learned the examined body region from a set of used MRI acquisition parameters such as grid and voxel size and could classify body region information with a classification rate up to 94.7%. We compared Bayesian Network augmented Naïve Bayes, Decision Trees, and Neural Networks, and found Neural Networks resulting in the best classification rate.<br
CRIS-Team Scopus Importer:2022-06-05
Keywords: Classification; Data mining; Log file analysis; Pattern recognition; System usage

Classification of Breast Cancer Histology Images Using Transfer Learning - Vesal, Sulaiman; Ravikumar, Nishant; Davari, Amirabbas; Ellmann, Stephan; Maier, Andreas (ICIAR 2018: Image Analysis and Recognition, 2018)

Classification of Confocal Laser Endomicroscopic Images of the Oral Cavity to Distinguish Pathological from Healthy Tissue - Jaremenko, Christian; Maier, Andreas; Steidl, Stefan; Hornegger, Joachim; Oetter, Nicolai; Knipfer, Christian; Stelzle, Florian; Neumann, Helmut (Bildverarbeitung für die Medizin 2015, 2015)
Confocal laser endomicroscopy is a recently introduced advanced imaging technique which enables microscopic imaging of the mucosa in-vivo. This technique has already been applied successfully during diagnosis of gastrointestinal diseases. Whereas for this purpose several computer aided diagnosis approaches exist, we present a classification system that is able to differentiate between healthy and pathological images of the oral cavity. Varying textural features of small rectangular regions are evaluated using random forests and support vector machines. Preliminary results reach up to 99.2% classification rate. This indicates that an automatic classification system to differentiate between healthy and pathological mucosa of the oral cavity is feasible.
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.classi_8

Classification of incunable glyphs and out-of-distribution detection with joint energy-based models - Kordon, Florian Johannes; Weichselbaumer, Nikolaus; Herz, Randall; Mossman, Stephen; Potten, Edward; Seuret, Mathias; Mayr, Martin; Christlein, Vincent (International Journal on Document Analysis and Recognition, 2023)
Optical character recognition (OCR) has proved a powerful tool for the digital analysis of printed historical documents. However, its ability to localize and identify individual glyphs is challenged by the tremendous variety in historical type design, the physicality of the printing process, and the state of conservation. We propose to mitigate these problems by a downstream fine-tuning step that corrects for pathological and undesirable extraction results. We implement this idea by using a joint energy-based model which classifies individual glyphs and simultaneously prunes potential out-of-distribution (OOD) samples like rubrications, initials, or ligatures. During model training, we introduce specific margins in the energy spectrum that aid this separation and explore the glyph distribution’s typical set to stabilize the optimization procedure. We observe strong classification at 0.972 AUPRC across 42 lower- and uppercase glyph types on a challenging digital reproduction of Johannes Balbus’ Catholicon, matching the performance of purely discriminative methods. At the same time, we achieve OOD detection rates of 0.989 AUPRC and 0.946 AUPRC for OOD ‘clutter’ and ‘ligatures’ which substantially improves upon recently proposed OOD detection techniques. The proposed approach can be easily integrated into the postprocessing phase of current OCR to aid reproduction and shape analysis researc

Classification of leukemic B-Lymphoblast cells from blood smear microscopic images with an attention-based deep learning method and advanced augmentation techniques - Marzahl, Christian; Aubreville, Marc; Voigt, Jörn; Maier, Andreas (ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging, 2019)
Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer type characterized by an increased number of immature lymphocytes. It is one of the leading cancer inflicted causes of death among children and affected around 876.000 people globally in 2015. In this work, we describe methods used for the “Classification of Normal versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images” ISBI challenge. Due to the morphological similarity between malignant and normal cells, the classification of these cell types is a very challenging task, even for human experts. Deep learning using Convolutional Neural Networks (CNN) provides state-of-the-art techniques for image classification, even with limited annotation data, based on techniques like transfer learning and data augmentation. Our solution to tackle this classification problem is based upon advanced augmentation techniques to counter overfitting the small dataset and transfer learning with adaptive learning rates. Additionally, we incorporated a basic attention mechanism based on a region proposal subnetwork to boost our results for this competition. The ResNet 18 network with an additional regression head achieved a weighted F1 score of 0.8284 on the final test set and 0.8746 on the preliminary test set.
CRIS-Team Scopus Importer:2020-02-21
Keywords: Acute Lymphoblastic Leukemia; Attention modeling; Deep learning

Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning - Bhandary Panambur, Adarsh; Madhu, Prathmesh; Maier, Andreas (2025)
Automatic identification of patients with luminal and non-luminal subtypes during a routine mammography screening can support clinicians in streamlining breast cancer therapy planning. Recent machine learning techniques have shown promising results in molecular subtype classification in mammography; however, they are highly dependent on pixel-level annotations, handcrafted, and radiomic features. In this work, we provide initial insights into the luminal subtype classification in full mammogram images trained using only image-level labels. Transfer learning is applied from a breast abnormality classification task, to finetune a ResNet-18-based luminal versus non-luminal subtype classification task. We present and compare our results on the publicly available CMMD dataset and show that our approach significantly outperforms the baseline classifier by achieving a mean AUC score of 0.6688 and a mean F1 score of 0.6693 on the test dataset. The improvement over baseline is statistically significant, with a p-value of p<0.0001.<br
https://cris.fau.de/converis/publicweb/Publication/320503560

Classification of Mitotic Cells - Potentials Beyond the Limits of Small Data Sets - Krappmann, Maximilian; Aubreville, Marc; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas (Bildverarbeitung für die Medizin 2018. Informatik aktuell, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.classi_9

Classification of Parkinson's Disease Patients-A Deep Learning Strategy - Carvajal-Castano, Helber Andres; Pérez Toro, Paula Andrea; Orozco Arroyave, Juan Rafael (Electronics, 2022)
(1) Background and objectives: Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases whose typical symptoms include bradykinesia, abnormal gait and posture, shortened strides, and other movement disorders. In this study, we present a novel framework to evaluate PD gait patterns using state of the art deep learning algorithms. A comparative analysis with three different approaches is presented and evaluated upon three groups of subjects: PD patients, Young Healthy Controls (YHC), and Elderly Healthy Controls (EHC). (2) Methods: The three approaches used in the study include: (i) The energy content of the gait signals in the frequency domain is captured with spectrograms that are used to feed a CNN model, (ii) Temporal information is incorporated by creating GRU networks, (iii) Temporal and spectral information is simultaneously captured by creating a new architecture based on CNNs and GRUs. (3) Results: Accuracies of up to 83.7% and 92.7% are found for the classification between PD vs. EHC and PD vs. YHC, respectively. According to our observations, the proposed approach based on the combination of temporal and spectral information, yields better results than others reported in the state of the art. (4) Conclusions: The results obtained in this study suggest that the combination of temporal and spectral information is more accurate than individual approaches used to classify and evaluate gait patterns in PD patients. To the best of our knowledge, this is the first study in gait analysis where temporal and spectral information is combined in an architecture of deep learning.
CRIS-Team WoS Importer:2022-09-23
Keywords: Parkinson’s disease; gait analysis; gate recurrent units; deep learning; convolutional neural networks

Classification of Polyethylene Particles and the Local CD3+ Lymphocytosis in Histological Slice Images - Steffes, Lara-Maria; Aubreville, Marc; Sesselmann, Stefan; Krenn, Veit; Maier, Andreas (Bildverarbeitung für die Medizin 2018. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.classi_2

Classification of Poverty Condition Using Natural Language Processing - Muneton-Santa, Guberney; Escobar-Grisales, Daniel; Orlando Lopez-Pabon, Felipe; Pérez Toro, Paula Andrea; Orozco Arroyave, Juan Rafael (Social Indicators Research, 2022)
This work introduces a methodology to classify between poor and extremely poor people through Natural Language Processing. The approach serves as a baseline to understand and classify poverty through the people's discourses using machine learning algorithms. Based on classical and modern word vector representations we propose two strategies for document level representations: (1) document-level features based on the concatenation of descriptive statistics and (2) Gaussian mixture models. Three classification methods are systematically evaluated: Support Vector Machines, Random Forest, and Extreme Gradient Boosting. The fourth best experiments yielded around 55% of accuracy, while the embeddings based on GloVe word vectors yielded a sensitivity of 79.6% which could be of great interest for the public policy makers to accurately find people who need to be prioritized in social programs.
CRIS-Team WoS Importer:2022-02-18

CLASSIFICATION OF PSORIATIC ARTHRITIS, SERONEGATIVE RHEUMATOID ARTHRITIS, AND SEROPOSITIVE RHEUMATOID ARTHRITIS USING DEEP LEARNING ON MAGNETIC RESONANCE IMAGING - Folle, Lukas; Bayat, Sara; Kleyer, Arnd; Fagni, Filippo; Kapsner, Lorenz; Schlereth, Maja; Meinderink, Timo; Breininger, Katharina; Tascilar, Koray; Krönke, Gerhard; Uder, Michael; Sticherling, Michael; et al. (Supplement 1, 2022)
Background: While MRI evaluation of joints has been primarily used to quantify inflammation at a cross-sectional and longitudinal level, less is known about the potential of MRI in distinguishing different patterns of inflammation in the various forms of arthritis. Objectives: To evaluate (i) whether deep learning using neural networks can be trained to distinguish between seropositive rheumatoid arthritis (RA+), seronegative RA (RA-), and psoriatic arthritis (PsA) based on structural inflammatory patterns on hand magnetic resonance imaging and (ii) to assess if psoriasis patients with subclinical inflammation fit into such patterns. Methods: ResNet 3D [1] neural networks were trained to distinguish (i) RA+ vs. PsA, (ii) RA- vs. PsA and (iii) RA+ vs. RA- with respect to hand MRI data. Diagnosis of patients was determined using the following guidelines: ACR/EULAR 2010 [2] for RA and CASPAR [3] for PsA. Results from T1 coronal, T2 coronal, T1 coronal and axial fat suppressed contrast-enhanced (CE) and T2 fat suppressed axial sequences were used. The performance of such trained networks was analyzed by the area-under-the-receiver-operating-characteristic curve (AUROC) with and without imputation of demographic and clinical parameters ( Figure 1A ). Additionally, the trained networks were applied to psoriasis patients without clinical signs of PsA. Results: MRI scans from 649 patients (135 RA-, 190 RA+, 177 PsA, 147 psoriasis) were included ( Table 1 ). The AUROC for differentiation between disease entities was 75% (SD 3%) for RA+ vs. PsA, 74% (SD 8%) for RA- vs. PsA, and 67% (6%) for RA+ vs. RA-. All MRI sequences were relevant for classification, however, when deleting CE sequences, the loss of performance was only marginal. The addition of patient-specific data to the networks did not provide significant improvements. Increasing amounts of training data demonstrated improved performance of the networks ( Figure 1B ). Psoriasis patients were mostly assigned to PsA by the neural networks, suggesting that PsA-like MRI pattern may be present early in the course of psoriatic disease. Conclusion: Deep learning can be successfully applied to differentiate MRI inflammatory patterns related to RA+, RA-, and PsA. Early changes in psoriasis patients can be recognized by neural networks and are characterized by a pattern that allowed the networks to classify them as PsA. <
Keywords: Rheumatoid arthritis; deep learning; neural networks

Classification of stuttering – The ComParE challenge and beyond - Bayerl, Sebastian P.; Gerczuk, Maurice; Batliner, Anton; Bergler, Christian; Amiriparian, Shahin; Schuller, Björn; Nöth, Elmar; Riedhammer, Korbinian (Computer Speech and Language, 2023)
The ACM Multimedia 2022 Computational Paralinguistics Challenge (ComParE) featured a sub-challenge on the classification of stuttering in order to bring attention to this important topic and engage a wider research community. Stuttering is a complex speech disorder characterized by blocks, prolongations of sounds and syllables, and repetitions of sounds and words. Accurately classifying the symptoms of stuttering has implications for the development of self-help tools and specialized automatic speech recognition systems (ASR) that can handle atypical speech patterns. This paper provides a review of the challenge contributions and improves upon them with new state-of-the-art classification results for the KSF-C dataset, and explores cross-language training to demonstrate the potential of datasets in multiple languages. To facilitate further research and reproducibility, the full KSF-C dataset, including test-set labels, is also released.
CRIS-Team Scopus Importer:2023-04-28
Keywords: ComParE challenge; Dysfluency; Paralinguistics; Pathological speech; Stuttering

Classification With Truncated l1 Distance Kernel - Huang, Xiaolin; Suykens, Johan A. K.; Wang, Shuning; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Neural Networks, 2018)
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.classi_7
Keywords: Classification; indefinite kernel; piecewise linear (PWL)

CLC: Complex Linear Coding for the DNS 2020 Challenge - Schröter, Hendrik; Rosenkranz, Tobias; Escalante Banuelos, Alberto; Maier, Andreas (2020)
Complex-valued processing brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the noise reduction process is based on a time-frequency (TF) mask which is applied to a noisy spectrogram. Complex masks (CM) usually outperform real-valued masks due to their ability to modify the phase. Recent work proposed to use a complex linear combination of coefficients called complex linear coding (CLC) instead of a point-wise multiplication with a mask. This allows to incorporate information from previous and optionally future time steps which results in superior performance over mask-based enhancement for certain noise conditions. In fact, the linear combination enables to model quasi-steady properties like the spectrum within a frequency band. In this work, we apply CLC to the Deep Noise Suppression (DNS) challenge and propose CLC as an alternative to traditional mask-based processing, e.g. used by the baseline. We evaluated our models using the provided test set and an additional validation set with real-world stationary and non-stationary noises. Based on the published test set, we outperform the baseline w.r.t. the scale independent signal distortion ratio (SI-SDR) by about 3dB.
Keywords: speech enhancement; noise reduction; recurrent neural networks

CLCNet: Deep learning-based noise reduction for hearing aids using complex linear coding - Schröter, Hendrik; Rosenkranz, Tobias; Escalante Banuelos, Alberto; Aubreville, Marc; Maier, Andreas (ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020)
Noise reduction is an important part of modern hearing aids and is included in most commercially available devices. Deep learning-based state-of-the-art algorithms, however, either do not consider real-time and frequency resolution constrains or result in poor quality under very noisy conditions. To improve monaural speech enhancement in noisy environments, we propose CLCNet, a framework based on complex valued linear coding. First, we define complex linear coding (CLC) motivated by linear predictive coding (LPC) that is applied in the complex frequency domain. Second, we propose a framework that incorporates complex spectrogram input and coefficient output. Third, we define a parametric normalization for complex valued spectrograms that complies with low-latency and on-line processing. Our CLCNet was evaluated on a mixture of the EUROM database and a real-world noise dataset recorded with hearing aids and compared to traditional real-valued Wiener-Filter gains.
Keywords: noise reduction; speech enhancement; LPC; hearing aid signal processing; deep learning

Clinical prototype implementation enabling an improved day-to-day mammography compression - Hertel, Madeleine; Liu, Chang; Song, Haobo; Golatta, Michael; Kappler, Steffen; Nanke, Ralf; Radicke, Marcus; Maier, Andreas; Rose, Georg (Physica Medica, 2023)
Purpose: In mammography, breast compression is achieved by lowering a compression paddle on the breast. Despite the directive that compression is needed, there is no concrete guideline on its execution. To estimate the degree of compression, current mammography units only provide compression force and breast thickness as parameters. Therefore, radiographers could be induced to mainly determine the level of compression based on compression force and apply the same value to all breast sizes. In this case, smaller breast sizes are exposed to higher pressure. This results in a highly varying perception of discomfort or even pain during the procedure, depending on the breast size. Methods: To overcome this imbalance, current research results suggest that pressure might be a more qualified parameter for a more uniform compression among all breast sizes. To utilize pressure, the contact area between breast and compression paddle must be determined. In this paper, we present an easy-to-implement prototype enabling a real-time pressure-based measure without the need of direct patient contact. Using an optical camera, the contact area between the breast and the compression paddle is automatically segmented by a deep learning model. Results: The model provides a mean pixel accuracy of 96.7% (SD: 2.3%), mean frequency-weighted intersection over union of 88.5% (SD: 6.3%), and a Dice score of 93.6% (SD: 2.2%). The subsequent pressure display is updated more than five times per second which enables the use in clinical routines to set the compression level. Conclusion: This prototype could help guiding to an improved breast compression routine in mammography procedures.
CRIS-Team Scopus Importer:2023-01-20
Keywords: Breast imaging; Compression; Deep-learning; Mammography

Closing the Calibration Loop: An Inside-Out-Tracking Paradigm for Augmented Reality in Orthopedic Surgery - Hajek, Jonas; Unberath, Mathias; Fotouhi, Javad; Bier, Bastian; Lee, Sing Chun; Osgood, Greg; Maier, Andreas; Armand, Mehran; Navab, Nassir (MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV, 2018)
CRIS-Team WoS Importer:2019-08-09

CoachLea: an Android Application to Evaluate the Speech Production and Perception of Children with Hearing Loss - Schäfer, Paula; Pérez Toro, Paula Andrea; Klumpp, Philipp; Orozco-Arroyave, Juan Rafael; Nöth, Elmar; Maier, Andreas; Abad, Alberto; Schuster, Maria; Arias Vergara, Tomás (23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022, 2022)
Keywords: Cochlear implant; Smartphone application; Speech production; Hearing aid; Hearing loss

CoachLea: an Android Application to Evaluate the Speech Production and Perception of Children with Hearing Loss - Schäfer, Paula; Pérez Toro, Paula Andrea; Klumpp, Philipp; Orozco Arroyave, Juan Rafael; Nöth, Elmar; Maier, Andreas; Abad, A.; Schuster, M.; Arias Vergara, Tomás (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2022)
Hearing loss can affect children's language, speech, and social skills development. Hearing problems can result from impaired auditory feedback due to various reasons such as trauma, a clinical condition, genetic alterations, and infections, among others. Early treatment is the key to successful hearing and speech rehabilitation if the hearing loss occurs before or during spoken language acquisition. In this work, we present CoachLea, an Android application to support the clinical evaluation and therapy of the speech production and perception of children with hearing loss. The app includes numerous daily exercises to capture speech and hearing data continuously and longitudinally using a game-like user interface. Speech exercises include the “Snail race”, “Animal Sounds”, and “Image identification”, whereas the hearing exercise consists of a word identification game based on minimal pairs with speech-in-noise recordings. In the long term, CoachLea aims to be a tool that supports the therapy of children with hearing loss.
CRIS-Team Scopus Importer:2022-10-28
Keywords: Cochlear implant; Hearing aid; Hearing loss; Smartphone application; Speech production

Coincidence detection and integration behavior in spiking neural networks - Stoll, Andreas; Maier, Andreas; Krauß, Patrick; Gerum, Richard; Schilling, Achim (Cognitive Neurodynamics, 2023)
Recently, the interest in spiking neural networks (SNNs) remarkably increased, as up to now some key advances of biological neural networks are still out of reach. Thus, the energy efficiency and the ability to dynamically react and adapt to input stimuli as observed in biological neurons is still difficult to achieve. One neuron model commonly used in SNNs is the leaky-integrate-and-fire (LIF) neuron. LIF neurons already show interesting dynamics and can be run in two operation modes: coincidence detectors for low and integrators for high membrane decay times, respectively. However, the emergence of these modes in SNNs and the consequence on network performance and information processing ability is still elusive. In this study, we examine the effect of different decay times in SNNs trained with a surrogate-gradient-based approach. We propose two measures that allow to determine the operation mode of LIF neurons: the number of contributing input spikes and the effective integration interval. We show that coincidence detection is characterized by a low number of input spikes as well as short integration intervals, whereas integration behavior is related to many input spikes over long integration intervals. We find the two measures to linearly correlate via a correlation factor that depends on the decay time. Thus, the correlation factor as function of the decay time shows a powerlaw behavior, which could be an intrinsic property of LIF networks. We argue that our work could be a starting point to further explore the operation modes in SNNs to boost efficiency and biological plausibility.
CRIS-Team Scopus Importer:2023-12-22
Keywords: Artificial intelligence; Coincidence detection; Computational modeling; Leaky-integrate-and-fire neuron; Neural networks

Collaborative training of medical artificial intelligence models with non-uniform labels - Tayebi Arasteh, Soroosh; Isfort, Peter; Saehn, Marwin; Mueller-Franzes, Gustav; Khader, Firas; Jakob Nikolas, Kather; Christiane, Kuhl; Sven, Nebelung; Truhn, Daniel (Scientific Reports, 2023)
Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training with large multi-party datasets. While multiple stakeholders have provided publicly available datasets, the ways in which these data are labeled vary widely. For Instance, an institution might provide a dataset of chest radiographs containing labels denoting the presence of pneumonia, while another institution might have a focus on determining the presence of metastases in the lung. Training a single AI model utilizing all these data is not feasible with conventional federated learning (FL). This prompts us to propose an extension to the widespread FL process, namely flexible federated learning (FFL) for collaborative training on such data. Using 695,000 chest radiographs from five institutions from across the globe—each with differing labels—we demonstrate that having heterogeneously labeled datasets, FFL-based training leads to significant performance increase compared to conventional FL training, where only the uniformly annotated images are utilized. We believe that our proposed algorithm could accelerate the process of bringing collaborative training methods from research and simulation phase to the real-world applications in healthcare.

Co-localized augmented human and X-ray observers in collaborative surgical ecosystem - Fotouhi, Javad; Unberath, Mathias; Song, Tianyu; Hajek, Jonas; Lee, Sing Chun; Bier, Bastian; Maier, Andreas; Osgood, Greg; Armand, Mehran; Navab, Nassir (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose: Image-guided percutaneous interventions are safer alternatives to conventional orthopedic and trauma surgeries. To advance surgical tools in complex bony structures during these procedures with confidence, a large number of images is acquired. While image-guidance is the de facto standard to guarantee acceptable outcome, when these images are presented on monitors far from the surgical site the information content cannot be associated easily with the 3D patient anatomy. Methods: In this article, we propose a collaborative augmented reality (AR) surgical ecosystem to jointly co-localize the C-arm X-ray and surgeon viewer. The technical contributions of this work include (1) joint calibration of a visual tracker on a C-arm scanner and its X-ray source via a hand-eye calibration strategy, and (2) inside-out co-localization of human and X-ray observers in shared tracking and augmentation environments using vision-based simultaneous localization and mapping. Results: We present a thorough evaluation of the hand-eye calibration procedure. Results suggest convergence when using 50 pose pairs or more. The mean translation and rotation errors at convergence are 5.7 mm and 0. 26 ∘, respectively. Further, user-in-the-loop studies were conducted to estimate the end-to-end target augmentation error. The mean distance between landmarks in real and virtual environment was 10.8 mm. Conclusions: The proposed AR solution provides a shared augmented experience between the human and X-ray viewer. The collaborative surgical AR system has the potential to simplify hand-eye coordination for surgeons or intuitively inform C-arm technologists for prospective X-ray view-point planning.
CRIS-Team Scopus Importer:2019-08-06
Keywords: Augmented reality; C-arm; Calibration; Surgery; X-ray

Combination of Markerless Surrogates for Motion Estimation in Radiation Therapy - Geimer, Tobias; Unberath, Mathias; Taubmann, Oliver; Bert, Christoph; Maier, Andreas (Computer Assisted Radiology and Surgery (CARS) 2016: Proceedings of the 30th International Congress and Exhibition, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.combin_2
Keywords: Motion Modeling; X-ray; Range Imaging; Dimensionality Reduction; Regression

Combining 2-D and 3-D weight-bearing x-ray images: Application to preoperative implant planning in the knee - Luckner, Christoph; Herbst, Magdalena; Fuhrmann, Michael; Ritschl, Ludwig; Kappler, Steffen; Maier, Andreas (Informatik aktuell, 2020)
Osteoarthritis is a joint disease that commonly affects the hands, feet, spine, as well as the large weight-bearing joints, i. e., the hip, and knees. Worldwide, about 3.6% of the population suffer from osteoarthritis of the knee. If the symptoms are too severe to be treated with medication, the solution is often a total replacement. The precise preoperative planning of implants is a crucial task to achieve a good patient outcome. For planning, usually hybrid 2-D/3-D approaches are used. The main drawback of theses hybrid methods is the different patient positions during the acquisition, namely lying for the 3-D scan and standing for the 2-D scan. We proposed a method that allows acquiring both images in standing positions under natural weight-bearing without having to reposition the patient. To show the feasibility, we provide images from an anthropomorphic leg phantom. A preliminary study with medical experts has shown that the results are promising.
CRIS-Team Scopus Importer:2020-04-21

Combining OCR Models for Reading Early Modern Books - Seuret, Mathias; van der Loop, Janne; Weichselbaumer, Nikolaus; Mayr, Martin; Molnar, Janina; Hass, Tatjana; Christlein, Vincent (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
In this paper, we investigate the usage of fine-grained font recognition on OCR for books printed from the 15th to the 18th century. We used a newly created dataset for OCR of early printed books for which fonts are labeled with bounding boxes. We know not only the font group used for each character, but the locations of font changes as well. In books of this period, we frequently find font group changes mid-line or even mid-word that indicate changes in language. We consider 8 different font groups present in our corpus and investigate 13 different subsets: the whole dataset and text lines with a single font, multiple fonts, Roman fonts, Gothic fonts, and each of the considered fonts, respectively. We show that OCR performance is strongly impacted by font style and that selecting fine-tuned models with font group recognition has a very positive impact on the results. Moreover, we developed a system using local font group recognition in order to combine the output of multiple font recognition models, and show that while slower, this approach performs better not only on text lines composed of multiple fonts but on the ones containing a single font only as well.
CRIS-Team Scopus Importer:2023-10-20
Keywords: Early modern prints; Historical documents; OCR

Combining Ultrasound and X-Ray Imaging for Mammography: A Prototype Design - Li, Qiuting; Luckner, Christoph; Hertel, Madeleine; Radicke, Marcus; Maier, Andreas (Informatik aktuell, 2019)
This study aims at the combination of 3D breast ultrasound and 2D mammography images to improve the accuracy of diagnosis of breast cancer. It was shown that ultrasound breast imaging has advantages for differentiating cysts and solid masses which are not visible in an X-ray image. Moreover, the specificity in X-ray imaging decreases with an increasing breast thickness, so that ultrasound is usually used as an adjunct to X-ray breast imaging. A fully automatic system to obtain both 2D mammography and 3D ultrasound images is used. The alignment of a 2D mammography image in the cone-beam coordinate system and 3D ultrasound image in a Cartesian coordinate system is the essential task in this study. We have shown that deviations up to 23 mm caused by the cone-beam system can be calculated and corrected utilizing the geometry information of the hardware. The multimodal image reading tool is presented in a GUI for clinical diagnosis. The presented setup might lead to a distinct improvement in efficiency and add diagnostic value to the acquisition.
CRIS-Team Scopus Importer:2019-05-14

Combining Ultrasound and X-Ray Imaging for Mammography - Li, Qiuting; Luckner, Christoph; Hertel, Madeleine; Radicke, Marcus; Maier, Andreas (Bildverarbeitung für die Medizin 2019, 2019)

Combining Visual and Linguistic Models for a Robust Recipient Line Recognition in Historical Documents - Mayr, Martin; Felker, Alex; Maier, Andreas; Christlein, Vincent (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
Automatically extracting targeted information from historical documents is an important task in the field of document analysis and eases the work of historians when dealing with huge corpora. In this work, we investigate the idea of retrieving the recipient transcriptions from the Nuremberg letterbooks of the 15th century. This task can be solved with fundamentally different ways of approaching it. First, detecting recipient lines solely based on visual features and without any explicit linguistic feedback. Here, we use a vanilla U-Net and an attention-based U-Net as representatives. Second, linguistic feedback can be used to classify each line accordingly. This is done on the one hand with handwritten text recognition (HTR) for predicting the transcriptions and on top of it a light-wight natural language processing (NLP) model distinguishing whether the line is a recipient line or not. On the other hand, we adapt a named entity recognition transformer model. The system jointly performs the line transcription and the recipient line recognition. For improving the performance, we investigated all the possible combinations with the different methods. In most cases the combined output probabilities outperformed the single approaches. The best combination achieved on the hard test set an F1 score of 80% and recipient line recognition accuracy of about 96% while the best single approach only reached about 74% and 94%, respectively.
CRIS-Team Scopus Importer:2022-06-10
Keywords: Handwritten text recognition; Natural language processing; Recipient recognition; Semantic image segmentation

Common Phone: A Multilingual Dataset for Robust Acoustic Modelling - Klumpp, Philipp; Arias-Vergara, Tomas; Perez-Toro, Paula-Andrea; Nöth, Elmar; Orozco-Arroyave, Juan Rafael (LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, 2022)
CRIS-Team WoS Importer:2023-02-24

Compact Convolutional Transformers on Edge TPUs - Sun, Yipeng; Kist, Andreas (Bildverarbeitung für die Medizin Workshop, BVM 2023, 2023)

Comparability of Deep Learning Techniques for Calving Front Segmentation in SAR Imagery - Gourmelon, Nora; Seehaus, Thorsten; Braun, Matthias; Maier, Andreas; Christlein, Vincent (European Geoscience Union General Assembly 2023, 2023)

Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data using Graph Neural Networks - Rivera Monroy, Luis; Rist, Leonhard; Eberhardt, Martin; Ostalecki, Christian; Bauer, Andreas; Vera, Julio; Breininger, Katharina; Maier, Andreas (Bildverarbeitung für die Medizin 2024, 2024)
This study leverages graph neural networks to integrate MELC data with Radiomic-extracted features for melanoma classification, focusing on cellwise analysis. It assesses the effectiveness of gene expression profiles and Radiomic features, revealing that Radiomic features, particularly when combined with UMAP for dimensionality reduction, significantly enhance classification performance. Notably, using Radiomics contributes to increased diagnostic accuracy and computational efficiency, as it allows for the extraction of critical data from fewer stains, thereby reducing operational costs. This methodology marks an advancement in computational dermatology for melanoma cell classification, setting the stage for future research and potential developments.<br

Comparative analysis of unsupervised algorithms for breast MRI lesion segmentation - Vesal, Sulaiman; Ravikumar, Nishant; Ellmann, Stephan; Maier, Andreas (Informatik aktuell, 2018)
Accurate segmentation of breast lesions is a crucial step in evaluating the characteristics of tumors. However, this is a challenging task, since breast lesions have sophisticated shape, topological structure, and variation in the intensity distribution. In this paper, we evaluated the performance of three unsupervised algorithms for the task of breast Magnetic Resonance (MRI) lesion segmentation, namely, Gaussian Mixture Model clustering, K-means clustering and a markercontrolled Watershed transformation based method. All methods were applied on breast MRI slices following selection of regions of interest (ROIs) by an expert radiologist and evaluated on 106 subject’s images, which include 59 malignant and 47 benign lesions. Segmentation accuracy was evaluated by comparing our results with ground truth masks, using the Dice similarity coefficient (DSC), Jaccard index (JI), Hausdorff distance and precision-recall metrics. The results indicate that the marker-controlled Watershed transformation outperformed all other algorithms investigated.
CRIS-Team Scopus Importer:2022-06-05

Comparative Evaluation of Interactive Segmentation Approaches - Amrehn, Mario; Glasbrenner, Jens; Steidl, Stefan; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
Image segmentation is a key technique in image processing with the goal to extract important objects from the image. This evaluation study focuses on the segmentation quality of three different interactive segmentation techniques, namely Region Growing, Watershed and the cellular automaton based GrowCut algorithm. Three different evaluation measures are computed to compare the segmentation quality of each algorithm: Rand Index, Mutual Information, and the Dice Coefficient. For the images in the publicly available ground truth data base utilized for the evaluation, the GrowCut method has a slight advantage over the other two. The presented results provide insight into the performance and the characteristics with respect to the image quality of each tested algorithm.
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.compar_6
Keywords: medical; image segmentation; 3-D; lesion; tumor; liver; hepatic; cancer

Comparative Evaluation of Two Registration-based Segmentation Algorithms: Application to Whole Heart Segmentation in CT - Unberath, Mathias; Maier, Andreas; Fleischmann, Dominik; Hornegger, Joachim; Fahrig, Rebecca (Proceedings of the GRC, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.compar

Comparative Study of Deep Learning Models for Optical Coherence Tomography Angiography - Jiang, Zhe; Huang, Zhiyu; Qiu, Bin; Liu, Xi; Meng, Xiangxi; You, Yunfei; Liu, Gangjun; Zhou, Chuanqing; Yang, Kun; Maier, Andreas; Ren, Qiushi; Lu, Yanye (Biomedical Optics Express, 2020)
Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Meanwhile, deep learning has achieved rapid development in image-to-image translation tasks. Some studies have proposed applying deep learning models to OCTA reconstruction and have obtained preliminary results. However, current studies are mostly limited to a few specific deep neural networks. In this paper, we conducted a comparative study to investigate OCTA reconstruction using deep learning models. Four representative network architectures including single-path models, U-shaped models, generative adversarial network (GAN)-based models and multi-path models were investigated on a dataset of OCTA images acquired from rat brains. Three potential solutions were also investigated to study the feasibility of improving performance. The results showed that U-shaped models and multi-path models are two suitable architectures for OCTA reconstruction. Furthermore, merging phase information should be the potential improving direction in further research.<br

Comparing performance of many-core CPUs and GPUs for static and motion compensated reconstruction of C-arm CT data - Hofmann, Hannes; Keck, Benjamin; Rohkohl, Christopher; Hornegger, Joachim (Medical Physics, 2011)
Purpose: Interventional reconstruction of 3-D volumetric data from C-arm CT projections is a computationally demanding task. Hardware optimization is not an option but mandatory for interventional image processing and, in particular, for image reconstruction due to the high demands on performance. Several groups have published fast analytical 3-D reconstruction on highly parallel hardware such as GPUs to mitigate this issue. The authors show that the performance of modern CPU-based systems is in the same order as current GPUs for static 3-D reconstruction and outperforms them for a recent motion compensated (3-D+time) image reconstruction algorithm. Methods: This work investigates two algorithms: Static 3-D reconstruction as well as a recent motion compensated algorithm. The evaluation was performed using a standardized reconstruction benchmark, RABBITCT, to get comparable results and two additional clinical data sets. Results: The authors demonstrate for a parametric B-spline motion estimation scheme that the derivative computation, which requires many write operations to memory, performs poorly on the GPU and can highly benefit from modern CPU architectures with large caches. Moreover, on a 32-core Intel® Xeon® server system, the authors achieve linear scaling with the number of cores used and reconstruction times almost in the same range as current GPUs. Conclusions: Algorithmic innovations in the field of motion compensated image reconstruction may lead to a shift back to CPUs in the future. For analytical 3-D reconstruction, the authors show that the gap between GPUs and CPUs became smaller. It can be performed in less than 20 s (on-the-fly) using a 32-core server. © 2011 American Association of Physicists in Medicine.

Comparison of Default Patient Surface Model Estimation Methods - Zhong, Xia; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Bildverarbeitung für die Medizin: Algorithmen - Systeme - Anwendungen, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.compar_5

Comparison of Different Approaches for Measuring Tibial Cartilage Thickness - Maier, Jennifer; Black, Marianne; Bonaretti, Serena; Bier, Bastian; Eskofier, Björn; Choi, Jang-Hwan; Levenston, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (Journal of integrative bioinformatics, 2017)
Osteoarthritis is a degenerative disease affecting bones and cartilage especially in the human knee. In this context, cartilage thickness is an indicator for knee cartilage health. Thickness measurements are performed on medical images acquired in-vivo. Currently, there is no standard method agreed upon that defines a distance measure in articular cartilage. In this work, we present a comparison of different methods commonly used in literature. These methods are based on nearest neighbors, surface normal vectors, local thickness and potential field lines. All approaches were applied to manual segmentations of tibia and lateral and medial tibial cartilage performed by experienced raters. The underlying data were contrast agent-enhanced cone-beam C-arm CT reconstructions of one healthy subject’s knee. The subject was scanned three times, once in supine position and two times in a standing weight-bearing position. A comparison of the resulting thickness maps shows similar distributions and high correlation coefficients between the approaches above 0.90. The nearest neighbor method results on average in the lowest cartilage thickness values, while the local thickness approach assigns the highest values. We showed that the different methods agree in their thickness distribution. The results will be used for a future evaluation of cartilage change under weight-bearing conditions.
Keywords: Cartilage strain; Cone-beam C-arm CT; Weight-bearing; Local thickness; Potential field lines

Comparison of intelligent 4D CT sequence scanning and conventional spiral 4D CT: A first comprehensive phantom study - Werner, René; Szkitsak, Juliane; Sentker, Thilo; Madesta, Frederic; Schwarz, Annette; Fernolendt, Susanne; Vornehm, Marc; Gauer, Tobias; Bert, Christoph; Hofmann, Christian (Physics in Medicine and Biology, 2021)
4D CT imaging is a cornerstone of 4D radiotherapy treatment. Clinical 4D CT data are, however, often affected by severe artifacts. The artifacts are mainly caused by breathing irregularity and retrospective correlation of breathing phase information and acquired projection data, which leads to insufficient projection data coverage to allow for proper reconstruction of 4D CT phase images. The recently introduced 4D CT approach i4DCT (intelligent 4D CT sequence scanning) aims to overcome this problem by breathing signal-driven tube control. The present motion phantom study describes the first in-depth evaluation of i4DCT in a real-world scenario. Twenty-eight 4D CT breathing curves of lung and liver tumor patients with pronounced breathing irregularity were selected to program the motion phantom. For every motion pattern, 4D CT imaging was performed with i4DCT and a conventional spiral 4D CT mode. For qualitative evaluation, the reconstructed 4D CT images were presented to clinical experts, who scored image quality. Further quantitative evaluation was based on established image intensity-based artifact metrics to measure (dis)similarity of neighboring image slices. In addition, beam-on and scan times of the scan modes were analyzed. The expert rating revealed a significantly higher image quality for the i4DCT data. The quantitative evaluation further supported the qualitative: While 20% of the slices of the conventional spiral 4D CT images were found to be artifact-affected, the corresponding fraction was only 4% for i4DCT. The beam-on time (surrogate of imaging dose) did not significantly differ between i4DCT and spiral 4D CT. Overall i4DCT scan times (time between first beam-on and last beam-on event, including scan breaks to compensate for breathing irregularity) were, on average, 53% longer compared to spiral CT. Thus, the results underline that i4DCT significantly improves 4D CT image quality compared to standard spiral CT scanning in the case of breathing irregularity during scanning.
CRIS-Team Scopus Importer:2021-02-12
Keywords: 4D CT; I4DCT; Motion artifacts; Radiotherapy

Comparison of methods for sensitivity correction in Talbot–Lau computed tomography - Felsner, Lina; Roser, Philipp; Maier, Andreas; Riess, Christian (International Journal of Computer Assisted Radiology and Surgery, 2021)
Purpose: In Talbot–Lau X-ray phase contrast imaging, the measured phase value depends on the position of the object in the measurement setup. When imaging large objects, this may lead to inhomogeneous phase contributions within the object. These inhomogeneities introduce artifacts in tomographic reconstructions of the object. Methods: In this work, we compare recently proposed approaches to correct such reconstruction artifacts. We compare an iterative reconstruction algorithm, a known operator network and a U-net. The methods are qualitatively and quantitatively compared on the Shepp–Logan phantom and on the anatomy of a human abdomen. We also perform a dedicated experiment on the noise behavior of the methods. Results: All methods were able to reduce the specific artifacts in the reconstructions for the simulated and virtual real anatomy data. The results show method-specific residual errors that are indicative for the inherently different correction approaches. While all methods were able to correct the artifacts, we report a different noise behavior. Conclusion: The iterative reconstruction performs very well, but at the cost of a high runtime. The known operator network shows consistently a very competitive performance. The U-net performs slightly worse, but has the benefit that it is a general-purpose network that does not require special application knowledge.
CRIS-Team Scopus Importer:2021-09-17
Keywords: computed tomography; known operator learning; sensitivity correction; Talbot–Lau interferometer; X-ray phase contrast imaging

Comparison of Rigid Gradient-Based 2D/3D Registration Using Projection and Back-Projection Strategies - Schaffert, Roman; Wang, Jian; Borsdorf, Anja; Hornegger, Joachim; Maier, Andreas (Bildverarbeitung für die Medizin, 2016)
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.compar_0

Comparison of SART and eTV Reconstruction for increased C-arm CT Volume Coverage by proper Detector Rotation in Liver Imaging - Stromer, Daniel; Amrehn, Mario; Huang, Yixing; Kugler, Patrick; Bauer, Sebastian; Lauritsch, Günter; Maier, Andreas (Proceedings of the 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016)
In this work, we present a method to increase the lateral field-of-view of a C-arm CT system by rotating the detector such that the diagonal of the detector lies on the u-axis of the detector's coordinate system. We investigated three different 3-D scan trajectories for liver imaging of an obese patient (waist circumference 130 cm) — a Short Scan, a Large Volume Scan and a Helical Large Volume Scan. We reconstructed a data set of the Visible Human Project with the SART and the eTV algorithm. Tests revealed that the coverage was increased with the presented method by 25.3 % for the Short Scan and 28.5 % for the Large Volume Scan. Performing helical scans compensated the axial data loss. The two implemented iterative approaches both provide acceptable results, with the eTV algorithm reducing the RMSE compared to SART by about 29 %. Given a liver imaging task, the rotated detector is able to image the entire liver section of the abdomen with a single Large Volume Sca
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.compar_1
Keywords: Diamond Scan; eTV; Field-of-View Enlargement; Iterative Reconstruction; iTV; SART

Compressed sensing for optical coherence tomography angiography volume generation - Husvogt, Lennart; Ploner, Stefan; Stromer, Daniel; Schottenhamml, Julia; Moult, Eric; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2020)
Optical coherence tomography angiography (OCTA) is an increasingly popular modality for imaging of the retinal vasculature. Repeated optical coherence tomography (OCT) scans of the retina allow the computation of motion contrast to display the retinal vasculature. To the best of our knowledge, we present the first application of compressed sensing for the generation of OCTA volumes. Using a probabilistic signal model for the computation of OCTA volumes and a 3D median filter, it is possible to perform compressed sensing reconstruction of OCTA volumes while suppressing noise. The presented approach was tested on a ground truth, averaged from ten individual OCTA volumes. Average reductions of the mean squared error of 9:67% were achieved when comparing reconstructed OCTA images to the stand-alone application of a 3D median filter.
CRIS-Team Scopus Importer:2020-04-21

Computed Tomography - Taubmann, Oliver; Berger, Martin; Bögel, Marco; Xia, Yan; Balda, Michael; Maier, Andreas (Medical Imaging Systems, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.comput

Computer Assistance: A New Gold Standard for the Mitotic Count? - Bertram, Christof A.; Aubreville, Marc; Donovan, Taryn A.; Bartel, Alexander; Wilm, Frauke; Marzahl, Christian; Assenmacher, Charles-Antoine; Becker, Kathrin; Bennett, Mark; Corner, Sarah; Cossic, Brieuc; Denk, Daniela; et al. (Journal of Comparative Pathology, 2022)

Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy - Bertram, Christof A.; Aubreville, Marc; Donovan, Taryn A.; Bartel, Alexander; Wilm, Frauke; Marzahl, Christian; Assenmacher, Charles-Antoine; Becker, Kathrin; Bennett, Mark; Corner, Sarah; Cossic, Brieuc; Denk, Daniela; et al. (Veterinary Pathology, 2022)
The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs.
CRIS-Team Scopus Importer:2022-03-25
Keywords: artificial intelligence; automated image analysis; canine cutaneous mast cell tumors; computer assistance; deep learning; digital pathology; mitotic count; mitotic figures

Computerized Calculation of Mitotic Count Distribution in Canine Cutaneous Mast Cell Tumor Sections: Mitotic Count Is Area Dependent - Bertram, Christof A.; Aubreville, Marc; Gurtner, Corinne; Bartel, Alexander; Corner, Sarah M.; Dettwiler, Martina; Kershaw, Olivia; Noland, Erica L.; Schmidt, Anja; Sledge, Dodd D.; Smedley, Rebecca C.; Thaiwong, Tuddow; et al. (Veterinary Pathology, 2020)
Mitotic count (MC) is an important element for grading canine cutaneous mast cell tumors (ccMCTs) and is determined in 10 consecutive high-power fields with the highest mitotic activity. However, there is variability in area selection between pathologists. In this study, the MC distribution and the effect of area selection on the MC were analyzed in ccMCTs. Two pathologists independently annotated all mitotic figures in whole-slide images of 28 ccMCTs (ground truth). Automated image analysis was used to examine the ground truth distribution of the MC throughout the tumor section area, which was compared with the manual MCs of 11 pathologists. Computerized analysis demonstrated high variability of the MC within different tumor areas. There were 6 MCTs with consistently low MCs (MC<7 in all tumor areas), 13 cases with mostly high MCs (MC ≥7 in ≥75% of 10 high-power field areas), and 9 borderline cases with variable MCs around 7, which is a cutoff value for ccMCT grading. There was inconsistency among pathologists in identifying the areas with the highest density of mitotic figures throughout the 3 ccMCT groups; only 51.9% of the counts were consistent with the highest 25% of the ground truth MC distribution. Regardless, there was substantial agreement between pathologists in detecting tumors with MC ≥7. Falsely low MCs below 7 mainly occurred in 4 of 9 borderline cases that had very few ground truth areas with MC ≥7. The findings of this study highlight the need to further standardize how to select the region of the tumor in which to determine the MC.<br
Keywords: area selection; high-power field; mitotic activity; mitotic figure distribution; tumor grading; tumor periphery

Computer Vision Understanding of Narrative Strategies on Greek Vases - Madhu, Prathmesh; Kosti, Ronak Vijaypal; Bendschus, Torsten; Reinhardt, Corinna; Maier, Andreas (Computer Applications and Quantitative Methods in Archaeology, 2020)
In this presentation we tackle the problem of understanding complex relationships and cultural references within artworks of Classical Antiquity by Computer Vision techniques using Machine Learning. Therefore, we approach the open challenge of understanding the semantics in images beyond the automated recognition of individual objects, by computationally identifying general formal schemes of human interaction. Our dataset consists of several thousands of images of Attic vase paintings from the 6th and 5th century BC. The specific case of vase paintings presents us with a very promising material basis because the understanding of iconography and visual narration in ancient imagery is a main focus in the scientific field of Classical Archaeology. Due to the fact that during this time the vase painters did not illustrate a textual version of myth, we have to look at the specific visual means they applied to narrate a story and to make it understandable for the viewer. These images make extensive use of highly meaningful and repetitive formal schemes as well as narrative elements. Both were used in order to characterize the individuals within the story as well as to make the situations in which these protagonists interact comprehensible for the ancient viewer [1]. On the one hand, the interaction and relationship of the figures is outlined by significant postures and gestures (schemata) in order to illustrate key aspects of the storyline. As these schemes are not limited to certain iconographies, visual links between different images and different image contexts occur [2]. Due to their familiarity with these schemes the ancient viewer was already able to identify the general meaning of the image and to understand the main components of a narration. On the other hand, however, other characteristic elements are needed to determine a depiction as, for example, a specific mythological story and to add further aspects of the narration. Hence, narrative elements such as attributes of gods, a certain surrounding or other significant objects in the image, are also essential to understand specific narrative contexts. Visual similarities are therefore the key for interpreting vase paintings. By comparing a large number of images, scholars usually study, at length, specific elements that help to narrate the stories. Although valuable insights are gained in terms of understanding the significance of the image relations for the cultural meaning of the vase paintings, unveiling links between images (mainly between non-narrative images – e.g. wedding scenes – and narrative ones) contributes even more details to our interpretation of these images and helps to comprehend their cultural meaning. Therefore, we aim to develop computational methods in order to support human research on the questions of archaeological image studies by Computer Vision and Deep Learning. Fresh perspectives on yet unidentified image relations and quantitative results by handling a large number of images are the main foci of our research, based not on the textual metadata but on the visual characteristics of an image. A selection of popular schemes in Attic vase paintings was chosen in order to answer the questions raised above: 1. Leading the bride, 2. Abduction, 3. Pursuit scenes. These schemes were deemed suitable as, for example, the scheme of leading the bride in Attic vase paintings is characterized by a significant leading-gesture (χεῖρ’ ἐπὶ καρπῷ – hand on wrist / hand on hand)that sets the bride and groom in non-mythological wedding scenes. However, also scenes of Helen and Menelaus make use of this scheme to show that Menelaus leads Helen home from Troy like a bride after regaining power over his wife. Therefore, not only the interaction scheme between the two figures must be recognized but also the narrative elements, i. e. Menelaus dressed as a warrior and his weapons, have to be identified in order to name the figures. We approach this problem of recognizing specific visual attributes using state-of-the-art object recognition algorithms in Computer Vision [3]. We present a novel approach using convolutional neural networks which maps the attributes occurring in these images into a deep representational space, through Deep Learning techniques. This representation maps the inherent variations in the representation of any particular attribute into a joint space. We recognize these objects using this embedding. However, attributes depicted in the narratives alone do not suffice. The gestures and poses of the characters highlight their role and importance in the narration. We model these characteristics of the protagonists in the deep neural network in conjunction with the recognition of the attributes. This also helps the network to recognize the narratives being displayed. Another novel method is developed that uses this representation space along with the context of the narrative for a better understanding of the depicted scene in the artwork. Here, we use the main theme of the scene (e. g. leading the bride or pursuit scene) as a context. We analyze the model performance with abstract classes where we combine multiple similar attributes into a single abstract class. This model learns a generic representation of the attributes through these abstract classes. This new approach has important applications, including the retrieval of semantically similar artworks and, in a more generic sense, contextual understanding of works of art in Classical Archaeology. Preliminary results of the recognition of attributes are promising. The network is able to predict most of the attributes present in the scene whose labels are available, and quite often predicts those attributes whose labels are missing. This is remarkable and a good representation of the generalization of the network, and serves as a proof of concept of our techniques mentioned above. To date, our proposed methods therefore have good potential to work towards the semantic understanding of the vase paintings. Due to its innovative and experimental approach we would not only like to present our first results to the audience, but particularly to discuss the occurring problems and especially the further potential of the applied methods as well. References: [1] Giuliani, L., 2003. Bild und Mythos. Geschichte der Bilderzählung in der griechischen Kunst, München: Beck 2003. [2] Catoni, M. L., 2008. La comunicazione non verbale nella Grecia antica : gli schemata nella danza, nell'arte, nella vita, Turin: Bollati Boringhieri. [3] Lin, T.Y., Goyal, P., Girshick, R., He, K. and Dollár, P., 2017. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980–2988).
Keywords: Attic vase painting; Computer Vision; Machine Learning

Computing Null Space Hallucinations on Simulated SPECT Image Reconstructions - Nau, Merlin; Reymann, Maximilian; Massanes, Francesc; Vija, Alexander Hans; Maier, Andreas (2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD), 2023)
Iterative reconstruction techniques have been integrated into clinical practice for emission tomography reconstruction. While these techniques have shown promising results in image reconstruction, there remains a lack of clarity regarding which parts of the image are reliably estimated and which are not. Currently, there is a missing measure to differentiate between epistemic and aleatoric errors. One approach to address this issue is to estimate the null space of the imaging model for a given object. In this study, we utilize a previously proposed adaptation of the Landweber iteration, known as the Wilson-Barret method, for calculating the null space of a noiseless reference phantom and for a corresponding reconstruction of a simulated phantom. Using the calculated null spaces we can apply a method for creating hallucination maps, that allow derivation of the epistemic error. Our findings suggest that the hallucinated components of the image are primarily attributable to the edges or high-frequency components of the phantom as well as the center of the phantom, which is consistent with expectations.<br

Concepts to Computational Constructs: Advanced Scene Understanding for Heterogeneous Artworks Using Deep Learning - Madhu, Prathmesh (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2023)
Due to the mass digitisation of paintings, manual examination and understanding of individual images is a cumbersome task. Developing automatic methods using computer vision and machine learning techniques is extremely useful for humanities experts, who are generally interested in understanding the origin of objects, iconographies, and narratives in artworks. Digital humanities has become a predominant field in the last decade for understanding and connecting the past, present, and future via artworks in the digitised form of text and images. The aim is to have quicker access to the information, uncover hidden trends and validate the theoretical learning from large data collections. Understanding artworks is challenging in digital humanities due to their subjective nature and lack of annotated data. Recently, deep learning-based methods have shown commendable performance on real-world images. One simple way is to learn algorithms on existing real-world photographs and test them on artwork images. Since the artwork images have a highly different data distribution, these algorithms often fail to generalise well, commonly referred to as the domain shift problem. This thesis develops several scene understanding methods from a digital humanities perspective, targeting Art history, Christian, and Classical archaeology domains. The focus lies on (a) developing methods for character-, iconography, and object recognition, and (b) beyond recognition, especially targeting pose-estimation and novel image compositions. Particular attention is given to methods beyond recognition, where the theoretical concepts from Art history are converted into a computational method for understanding and linking iconography. For methods in recognition, starting with recognising characters for Art History, a two-step style transfer learning algorithm is developed. This work is extended to iconography recognition, where a detailed analysis of the impact of styles using supervised and self-supervised models is presented. To mitigate the problem of the availability of few annotations, a one-shot object detection pipeline with advanced augmentations such as context and crop is developed for heterogeneous artworks. For methods beyond recognition, first, the task of linking narratives in Greek vase paintings is considered using pose estimation with as few as 1500 pose annotations. The proposed two-step style transfer learning for recognition is extended to enhance pose estimation and build a pose-based image retrieval system to link narratives in Classical archaeology. Finally, a novel computational algorithm is developed, namely Image Composition Canvas (ICC) which is an operationalisation based on compositions in paintings presented by Hetzer and extended by Max Imdahl to understand artworks. The concept of the mid-level feature extraction method presented by Imdahl is constructed and extended to an image retrieval system (ICC++) with explainable features. The proposed mid-level composition features are extremely lightweight and outperform the existing state-of-the-art, which only uses detected pose key points to link the images. The detailed qualitative and quantitative results show the potential to improve the image composition methods further to introduce complex composition features. This work, therefore, builds new constructs and proof of concepts for artwork scene understanding tasks, including recognition and beyond, allowing a detailed understanding of styles for domain adaptation from both digital humanities and computer vision perspectives.<br

Conceptual Cognitive Maps Formation with Neural Successor Networks and Word Embeddings - Stöwer, Paul; Schilling, Achim; Maier, Andreas; Krauß, Patrick (IEEE International Conference on Development and Learning 2023 (ICDL), 2023)

Conditional Random Fields for Improving Deep Learning-based Glacier Calving Front Delineations - Gourmelon, Nora; Klink, Julian; Seehaus, Thorsten; Braun, Matthias; Maier, Andreas; Christlein, Vincent (IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023)

Cone-beam CT trajectory optimization for metal artifact avoidance using ellipsoidal object parameterizations - Rohleder, Maximilian; Mekki, Lina; Uneri, Ali; Sisniega, Alex; Kunze, Holger; Kleinszig, Gerhard; Maier, Andreas; Kreher, Björn W.; Siewerdsen, Jeffrey H. (2023)
Metal Artifacts remain a problem in Cone-Beam CT (CBCT) imaging, especially reducing clinical value in trauma applications by obscuring the important area around implants. Building on existing Metal Artifact Avoidance (MAA) algorithms, we formulate a metric based on the scatter fraction, introduce a shape model which reduces computational requirements and analyze the feasibility of using only two scout views as prior information. Driven by the observation that orbit optimization requires knowledge of the position, extent, and orientation of metallic objects in the volume of interest (VOI), we devise a shape model in the form of ellipsoids. Reconstructing an ellipsoid from two projection images is not unambiguously possible using analytic methods. By interpreting the problem as probability density estimation, a maximum likelihood fit can be recovered using a Gaussian Mixture Model (GMM). This parametric representation of metal objects is used to efficiently calculate metal pathlength maps for candidate projections on tilted circular trajectories through analytic forward projection. The scatter fraction behind the metal object is modelled as a function of metal pathlength to score views and choose artifact minimizing tilted trajectories. Given two projection images of simulated ellipsoidal objects, the GMM accurately estimates the position and longest axis length within millimeter tolerance. Depending on the orientation relative to the two acquired scout views, an estimation error within the scout-view plane is observed. The generalization on measured data and the shape model hypothesis is verified in a phantom study showing a good correspondence of the modelled metric and observed reduction of artifacts in the tilted CBCT scans. The severity of Metal Artifacts can be reduced by optimizing trajectories on low-fidelity shape models. These surrogate representations can be efficiently estimated from two views for most relative object orientations, but a well-defined ‘blind spot’ remains. The reconstruction error was found to have little effect on tilted orbit optimization if the tilt-axis is contained in the scout-view plan

Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution - Bercea, Cosmin I.; Maier, Andreas; Köhler, Thomas (Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.confid
Keywords: Levenberg-Marquardt optimization; Motion estimation; Super-Resolütion

Confocal laser endomicroscopy of head and neck squamous cell carcinoma: a systematic review Konfokale Laser-Endomikroskopie des Kopf-Hals-Plattenepithelkarzinoms: eine systematische Übersicht - Sievert, Matti; Auberville, Marc; Oetter, Nicolai; Stelzle, Florian; Maier, Andreas; Mantsopoulos, Konstantinos; Iro, Heinrich; Goncalves, Miguel (Laryngo-Rhino-Otologie, 2021)
Objective: Confocal laser endomicroscopy (CLE), with a magnification of up to 1000 ×, offers the possibility to visualize intercellular spaces in vivo. CLE has already established itself in different disciplines. This article gives an overview of the current research on CLE in the diagnosis of the head and neck squamous cell carcinoma. Material and methods: Material and methods: Systematic bibliographic research in the following online databases: PubMed, MEDLINE, Thompson Reuters Web of Science, SPIE using the following keywords: confocal laser endomicroscopy, CLE, endomicroscopy, head and neck, larynx. Evaluation of the scientific relevance according to defined criteria. Results: All studies were analyzed concerning the clinical application, clinical findings, and computer-aided data processing. Conclusions: The recently published data suggest that CLE has a high potential to improve the diagnosis of malignant mucosal lesions in the upper aerodigestive tract.
CRIS-Team Scopus Importer:2021-01-22
Keywords: CLE; confocal laser endomicroscopy; head and neck malignancy; image analysis; laryngeal carcinoma

CONRAD - A software framework for cone-beam imaging in radiology - Maier, Andreas; Hofmann, Hannes; Berger, Martin; Fischer, Peter; Schwemmer, Chris; Wu, Haibo; Müller, Kerstin; Hornegger, Joachim; Choi, Jang-Hwan; Riess, Christian; Keil, Andreas; Fahrig, Rebecca (Medical Physics, 2013)

Consistency-based Respiratory Motion Estimation in Rotational Angiography - Unberath, Mathias; Aichert, André; Achenbach, Stephan; Maier, Andreas (Medical Physics, 2017)
Purpose: Rotational coronary angiography enables 3D reconstruction but suffers from intra-scan cardiac and respiratory motion. While gating handles cardiac motion, respiratory motion requires compensation. State-of-the-art algorithms rely on 3D-2D registration that depends on initial reconstructions of sufficient quality. We propose a compensation method that is applied directly in projection domain. It overcomes the need for reconstruction and thus complements the state-of-the-art. Methods: Virtual single-frame background subtraction based on vessel segmentation and spectral deconvolution yields non-truncated images of the contrasted lumen. This allows motion compensation based on data consistency conditions. We compensate craniocaudal shifts by optimizing epipolar consistency to (a) devise an image-based surrogate for cardiac motion and (b) compensate for respiratory motion. We validate our approach in two numerical phantom studies and three clinical cases. Results: Correlation of the image-based surrogate for cardiac motion with the ECG-based ground truth was excellent yielding a Pearson correlation of 0.93 ± 0.04. Considering motion compensation, the target error measure decreased by 98% and 69%, respectively, for the phantom experiments while for the clinical cases the same figure of merit improved by 46 ± 21%. Conclusions: The proposed method is entirely image-based and accurately estimates craniocaudal shifts due to respiration and cardiac contraction. Future work will investigate experimental trajectories and possibilities for simplification of the single-frame subtraction pipeline.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.consis
Keywords: cone-beam CT; inpainting; motion correction; vessel segmentation

Context Based Emotion Recognition using EMOTIC Dataset - Kosti, Ronak Vijaypal; et al. (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019)
In our everyday lives and social interactions we often try to perceive the emotional states of people. There has been a lot of research in providing machines with a similar capacity of recognizing emotions. From a computer vision perspective, most of the previous efforts have been focusing in analyzing the facial expressions and, in some cases, also the body pose. Some of these methods work remarkably well in specific settings. However, their performance is limited in natural, unconstrained environments. Psychological studies show that the scene context, in addition to facial expression and body pose, contributes important information to our perception of people's emotions. However, the processing of the context for automatic emotion recognition has not been explored in depth, partly due to the lack of proper data. In this paper we present EMOTIC, a dataset of images of people in natural and different situations annotated with their apparent emotion. The EMOTIC database combines two different types of emotion representation: (1) a set of 26 discrete categories, and (2) the continuous dimensions Valence, Arousal, and Dominance. We also present a detailed statistical and algorithmic analysis of the dataset along with annotators' agreement analysis. Using the EMOTIC database we train different CNN models for emotion recognition, combining the information of the person bounding box with the information present in the scene context. Our results show how scene context contributes important information to automatically recognize emotional states and motivate further research in this direction.<br
Keywords: Emotion recognition; Databases; Face; Face recognition; Observers; Computer vision

Context-sensitive organspecific evaluation and analysis of dual energy computed tomography (DECT) - Dorn, Sabrina; Chen, Shuqing; Pisana, Francesco; Özdemir, Mahmut; Maier, Joscha; Sawall, Stefan; Knaup, Michael; Maier, Andreas; Lell, Michael; Kachelriess, Marc (European Congress of Radiology (ECR), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.contex

Contextual HookFormer for Glacier Calving Front Segmentation - Wu, Fei; Gourmelon, Nora; Seehaus, Thorsten; Zhang, Jianlin; Braun, Matthias; Maier, Andreas; Christlein, Vincent (IEEE Transactions on Geoscience and Remote Sensing, 2024)
Position changes in glacier calving fronts are important indicators for evaluating the health of ice sheet outlet glaciers and changes in ice dynamics. However, manual delineation of calving fronts in remote sensing imagery is a time-consuming task, resulting in potential large costs. Deep-learning-based methods have made remarkable progress in automatically segmenting and delineating glacier calving fronts from remote sensing imagery. The relatively few remote sensing images and the limited geometric changes for glacier observations both reduce the diversity of the data and exacerbate the difficulty in accurate segmentation. Here we describe a novel automatic method for detecting glacier calving fronts in synthetic aperture radar (SAR) images, termed HookFormer. Our approach processes high-resolution (target) and low-resolution (context) inputs with a unified transformer architecture. The global–local tokens from the context and the target branches are integrated purely by the proposed cross-attention mechanism and cross-interaction module to complement and enhance each other. Moreover, we redesign the HookFormer architecture based on the convolutional neural network (CNN) model AMD-HookNet aiming to improve computational efficiency while achieving significant performance gains with only half of the model parameters/FLOPs. We conduct an in-depth analysis and make extensive comparisons based on the challenging glacier segmentation benchmark dataset CaFFe. As the first pure transformer approach, HookFormer sets a new state of the art with a mean distance error (MDE) of 353m to the ground truth, outperforming the baseline, Swin-Unet, and AMD-HookNet by 53%, 39%, and 19%, respectively.<br

Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study - Vysotskaya, Nastassia; Will, Christoph; Servadei, Lorenzo; Maul, Noah; Mandl, Christian; Nau, Merlin; Harnisch, Jens; Maier, Andreas (Sensors, 2023)
Blood pressure monitoring is of paramount importance in the assessment of a human’s cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations—it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used—together with the calibration parameters of age, gender, height, and weight—as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach’s predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of 9.2±8.3 mmHg (mean error ± standard deviation) and a diastolic error of 7.7±5.7 mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even furthe

Contour-Based Bone Axis Detection for X-Ray Guided Surgery on the Knee - Kordon, Florian Johannes; Maier, Andreas; Swartman, Benedict; Privalov, Maxim; El Barbari, Jan Siad; Kunze, Holger (Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020)
The anatomical axis of long bones is an important reference line for guiding fracture reduction and assisting in the correct placement of guide pins, screws, and implants in orthopedics and trauma surgery. This study investigates an automatic approach for detection of such axes on X-ray images based on the segmentation contour of the bone. For this purpose, we use the medically established two-line method and translate it into a learning-based approach. The proposed method is evaluated on 38 clinical test images of the femoral and tibial bone and achieves a median angulation error of 0.19° and 0.33° respectively. An inter-rater study with three trauma surgery experts confirms reliability of the method and recommends further clinical application.
Keywords: Surgical Planning; Orthopedics; Deep Learning

Contrast-based 3D/2D registration of the left atrium: Fast versus consistent - Hoffmann, Matthias; Kowalewski, Christopher; Maier, Andreas; Kurzidim, Klaus; Strobel, Norbert; Hornegger, Joachim (International Journal of Biomedical Imaging, 2016)
For augmented fluoroscopy during cardiac ablation, a preoperatively acquired 3D model of a patient's left atrium (LA) can be registered to X-ray images recorded during a contrast agent (CA) injection. An automatic registration method that works also for small amounts of CA is desired. We propose two similarity measures: The first focuses on edges of the patient anatomy. The second computes a contrast agent distribution estimate (CADE) inside the 3D model and rates its consistency with the CA as seen in biplane fluoroscopic images. Moreover, temporal filtering on the obtained registration results of a sequence is applied using a Markov chain framework. Evaluation was performed on 11 well-contrasted clinical angiographic sequences and 10 additional sequences with less CA. For well-contrasted sequences, the error for all 73 frames was 7.9 ± 6.3 mm and it dropped to 4.6 ± 4.0 mm when registering to an automatically selected, well enhanced frame in each sequence. Temporal filtering reduced the error for all frames from 7.9 ± 6.3 mm to 5.7 ± 4.6 mm. The error was typically higher if less CA was used. A combination of both similarity measures outperforms a previously proposed similarity measure. The mean accuracy for well contrasted sequences is in the range of other proposed manual registration methods.

Contrast-based 3-D/2-D Registration of the Left Atrium: Fast vs. Consistent - Hoffmann, Matthias; Kowalewski, Christopher; Maier, Andreas; Kurzidim, Klaus; Strobel, Norbert; Hornegger, Joachim (International Journal of Biomedical Imaging, 2016)
UnivIS-Import:2018-09-05:Pub.2016.tech.IMMD.IMMD5.contra_0

Contrast-Based Registration Of Left Atria To Fluoroscopic Image Sequences By Temporal Markow Filtering And Motion Regularization - Hoffmann, Matthias; Strobel, Norbert; Hornegger, Joachim; Maier, Andreas (Proceedings of the 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.contra

Convex Temporal Regularizers in Cardiac C-arm CT - Taubmann, Oliver; Lauritsch, Günter; Krings, Gregor; Maier, Andreas (Proceedings of the 4th International Conference on Image Formation in X-ray Computed Tomography, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.convex_0

Convolution-Based Truncation Correction for C-Arm CT Using Scattered Radiation - Bier, Bastian; Schwemmer, Chris; Maier, Andreas; Hofmann, Hannes; Xia, Yan; Hornegger, Joachim; Struffert, Tobias (Bildverarbeitung für die Medizin 2013, 2013)

Co-ordinate-based positional embedding that captures resolution to enhance transformer’s performance in medical image analysis - Das, Badhan Kumar; Zhao, Gengyan; Islam, Saahil; Re, Thomas J.; Comaniciu, Dorin; Maier, Andreas; Gibson, Eli (Scientific Reports, 2024)
Vision transformers (ViTs) have revolutionized computer vision by employing self-attention instead of convolutional neural networks and demonstrated success due to their ability to capture global dependencies and remove spatial biases of locality. In medical imaging, where input data may differ in size and resolution, existing architectures require resampling or resizing during pre-processing, leading to potential spatial resolution loss and information degradation. This study proposes a co-ordinate-based embedding that encodes the geometry of medical images, capturing physical co-ordinate and resolution information without the need for resampling or resizing. The effectiveness of the proposed embedding is demonstrated through experiments with UNETR and SwinUNETR models for infarct segmentation on MRI dataset with AxTrace and AxADC contrasts. The dataset consists of 1142 training, 133 validation and 143 test subjects. Both models with the addition of co-ordinate based positional embedding achieved substantial improvements in mean Dice score by 6.5% and 7.6%. The proposed embedding showcased a statistically significant advantage p-value< 0.0001 over alternative approaches. In conclusion, the proposed co-ordinate-based pixel-wise positional embedding method offers a promising solution for Transformer-based models in medical image analysis. It effectively leverages physical co-ordinate information to enhance performance without compromising spatial resolution and provides a foundation for future advancements in positional embedding techniques for medical applications.<br
Keywords: Biomedical engineering; Computer science; Engineering

COPD classification in CT images using a 3D convolutional neural network - Ahmed, Jalil; Vesal, Sulaiman; Durlak, Felix; Kaergel, Rainer; Ravikumar, Nishant; Rémy-Jardin, Martine; Maier, Andreas (Informatik aktuell, 2020)
Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world. Early detection and diagnosis of COPD can increase the survival rate and reduce the risk of COPD progression in patients. Currently, the primary examination tool to diagnose COPD is spirometry. However, computed tomography (CT) is used for detecting symptoms and sub-type classification of COPD. Using different imaging modalities is a diffcult and tedious task even for physicians and is subjective to inter-and intra-observer variations. Hence, developing methods that can automatically classify COPD versus healthy patients is of great interest. In this paper, we propose a 3D deep learning approach to classify COPD and emphysema using volume-wise annotations only. We also demonstrate the impact of transfer learning on the classification of emphysema using knowledge transfer from a pre-trained COPD classification model.
CRIS-Team Scopus Importer:2020-04-21

Coping with Real World Data: Artifact Reduction and Denoising for Motion-Compensated Cardiac C-arm CT - Taubmann, Oliver; Maier, Andreas; Hornegger, Joachim; Lauritsch, Günter; Fahrig, Rebecca (Medical Physics, 2016)
UnivIS-Import:2016-02-10:Pub.2016.tech.IMMD.IMMD5.coping
Keywords: Artifact Reduction; Motion-Compensated Reconstruction; 4-D Imaging; Cardiac C-arm CT

CoroEval: a multi-platform, multi-modality tool for the evaluation of 3D coronary vessel reconstructions - Schwemmer, Chris; Forman, Christoph; Wetzl, Jens; Maier, Andreas; Hornegger, Joachim (Physics in Medicine and Biology, 2014)
With default settings, measurements are taken at 1 mm intervals along the vessel centreline and from 10 different angles at each measurement point. This allows for outlier detection and noise-robust measurements without the burden and subjectivity a manual measurement process would incur. Graphical measurement results can be directly exported to vector or bitmap graphics for integration into scientific publications. Centreline and lumen segmentations can be exported as point clouds and in various mesh formats. We evaluated the diameter measurement process using three phantom datasets. An average deviation of 0.03 +/- 0.03 mm was found.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.coroev
Keywords: medical imaging; coronary vessels; evaluation; software