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Automatic Dating of Historical Documents - Christlein, Vincent; Gropp, Martin; Maier, Andreas (Kodikologie und Paläographie im digitalen Zeitalter, 2017)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 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

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 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 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

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

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

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

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

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 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 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 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 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, 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

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

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

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 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

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

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 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

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-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)

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

CoronARe: A coronary artery reconstruction challenge - Cimen, Serkan; Unberath, Mathias; Frangi, Alejandro; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017)
CoronARe ranks state-of-the-art methods in symbolic and tomographic coronary artery reconstruction from interventional C-arm rotational angiography. Specifically, we benchmark the performance of the methods using accurately pre-processed data, and study the effects of imperfect pre-processing conditions (segmentation and background subtraction errors). In this first iteration of the challenge, evaluation is performed in a controlled environment using digital phantom images, where accurate 3D ground truth is known.
CRIS-Team Scopus Importer:2022-06-05
Keywords: 3D reconstruction; Angiography; C-arm Cone-beam CT; Motion compensation

Coronary Artery Plaque Characterization from CCTA Scans Using Deep Learning and Radiomics - Denzinger, Felix; Wels, Michael; Ravikumar, Nishant; Breininger, Katharina; Reidelshöfer, Anika; Eckert, Joachim; Sühling, Michael; Schmermund, Axel; Maier, Andreas (MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, 2019)
Assessing coronary artery plaque segments in coronary CT angiography scans is an important task to improve patient management and clinical outcomes, as it can help to decide whether invasive investigation and treatment are necessary. In this work, we present three machine learning approaches capable of performing this task. The first approach is based on radiomics, where a plaque segmentation is used to calculate various shape-, intensity- and texture-based features under different image transformations. A second approach is based on deep learning and relies on centerline extraction as sole prerequisite. In the third approach, we fuse the deep learning approach with radiomic features. On our data the methods reached similar scores as simulated fractional flow reserve (FFR) measurements, which - in contrast to our methods - requires an exact segmentation of the whole coronary tree and often time-consuming manual interaction. In literature, the performance of simulated FFR reaches an AUC between 0.79–0.93 predicting an abnormal invasive FFR that demands revascularization. The radiomics approach achieves an AUC of 0.84, the deep learning approach 0.86 and the combined method 0.88 for predicting the revascularization decision directly. While all three proposed methods can be determined within seconds, the FFR simulation typically takes several minutes. Provided representative training data in sufficient quantities, we believe that the presented methods can be used to create systems for fully automatic non-invasive risk assessment for a variety of adverse cardiac events.<br
Keywords: Plaque characterization; Computer aided diagnosis; Coronary CT angiography; Radiomics; Deep learning

Coronary Plaque Analysis for CT Angiography Clinical Research - Denzinger, Felix; Wels, Michael; Hopfgartner, Christian; Lu, Jing; Schöbinger, Max; Maier, Andreas; Sühling, Michael (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Correction of artifacts from misregistered B-scans in orthogonally scanned and registered OCT angiography - Ploner, Stefan; Schottenhamml, Julia; Moult, Eric M.; Husvogt, Lennart; Alibhai, A. Yasin; Waheed, Nadia K.; Duker, Jay S.; Fujimoto, James G.; Maier, Andreas (Investigative Ophthalmology & Visual Science, 2019)
Purpose : While individual volumetric optical coherence tomography (OCT) scans suffer from distortion especially along the slowly scanned transverse axis, registration enables merging of two orthogonally oriented B-scans into one averaged volume with corrected distortion and reduced noise. However, registration errors can occur, which appear as blurred or duplicated vessels in en-face OCT angiography (OCTA) images. We present a fully automatic method that excludes artifacts and locally smooths resulting en-face images to achieve a uniform noise level. Noise non-uniformity emerges from differing numbers of averaged samples. This occurs when misregistrations or saccadic OCTA B-scans are excluded or due to varying depth of projected layers in en-face images. Uniform noise is critical for subsequent analysis algorithms to produce consistent results throughout the image. Methods : Two orthogonally scanned OCT volumes are registered and individual en-face OCTA images are computed. By design, errors in registration occur along B-scans, thus they extend along horizontal or vertical lines in the en-face images. As vessels do not coincide within misregistrations, these lines are detected via their high difference between the en-face images. The scan with B-scan orientation along the line can be identified as the one containing the misregistration and only that scan's B-scan is excluded, while the valid data in the other scan is preserved. To compensate for locally increased noise due to smaller numbers of averaged samples, adaptive Gaussian smoothing is applied. The standard deviation is controlled locally by the number of excluded scans and the remaining scans' sampling densities and projected layers' thickness, such that the total contribution of samples per pixel is kept uniform. The method was evaluated on 8 healthy eyes and 6 eyes with various pathologies. Results : Figure 1 shows the classification performance, Figure 2 compares merged images. Conclusions : The number of artifacts in orthogonal scan based OCTA volume registration was reduced while preventing noise non-uniformity due to varying numbers of averaged samples per pixe

Correction propagation for user-assisted optical coherence tomography segmentation: general framework and application to Bruch’s membrane segmentation - Stromer, Daniel; Moult, Eric M.; Chen, Siyu; Waheed, Nadja K.; Maier, Andreas; Fujimoto, James G. (Biomedical Optics Express, 2020)
Optical coherence tomography (OCT) is a commonly used ophthalmic imaging modality. While OCT has traditionally been viewed cross-sectionally (i.e., as a sequence of B-scans), higher A-scan rates have increased interest in en face OCT visualization and analysis. The recent clinical introduction of OCT angiography (OCTA) has further spurred this interest, with chorioretinal OCTA being predominantly displayed via en face projections. Although en face visualization and quantitation are natural for many retinal features (e.g., drusen and vasculature), it requires segmentation. Because manual segmentation of volumetric OCT data is prohibitively laborious in many settings, there has been significant research and commercial interest in developing automatic segmentation algorithms. While these algorithms have achieved impressive results, the variability of image qualities and the variety of ocular pathologies cause even the most robust automatic segmentation algorithms to err. In this study, we develop a user-assisted segmentation approach, complementary to fully-automatic methods, wherein correction propagation is used to reduce the burden of manually correcting automatic segmentations. The approach is evaluated for Bruch’s membrane segmentation in eyes with advanced age-related macular degeneration.<br
Keywords: Angiography; Image processing; Ophthalmic imaging; Optical coherence tomography; Retinal nerve fiber layer; Segmentation

Correction to: Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing (Magnetic Resonance Materials in Physics, Biology and Medicine, (2020), 10.1007/s10334-019-00819-2) - Utzschneider, Matthias; Behl, Nicolas G.R.; Lachner, Sebastian; Gast, Lena; Maier, Andreas; Uder, Michael; Nagel, Armin Michael (Magnetic Resonance Materials in Physics Biology and Medicine, 2020)
The original version of this article unfortunately contained a mistake. Title was incorrect. The corrected title is given below. Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing. The original article has been corrected.
CRIS-Team Scopus Importer:2020-02-25

Corrigendum to: Intraoperative imaging modalities and compensation for brain shift in tumor resection surgery (International Journal of Biomedical Imaging (2017) 2017 (6028645) DOI: 10.1155/2017/6028645) - Bayer, Siming; Maier, Andreas; Ostermeier, Martin; Fahrig, Rebecca (International Journal of Biomedical Imaging, 2019)
In the article titled "Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery" [1], there following two errors should be corrected: (1) The number of categories should be thirteen instead of fourteen in the following three sentences: (i) The sentence "In total, 126 papers regarding this topic are analyzed in a comprehensive summary and are categorized according to fourteen criteria" in the abstract section. (ii) The sentence "The fourteen categories of interest are brain shift compensation strategy, global transformation model, local transformation model, computational platform, registration basis, optimization method, similarity metric, intraoperative modality, constitutive model type, mesh element, quantification object, validation, and treatment (the categories in [13] are physical, surgical, biological, intraoperative imaging, other, registration, and modeling. This categorization focuses on the causes of brain shift rather than on the algorithmic approaches to identify and correct for brain shift)" in the Methods and Contribution section. (iii) The sentence "In total, 116 publications are grouped by the defined fourteen categories" in the Methods and Contribution section. (2) Reference number [132] should be changed as follows: F. L. Bookstein, "Principal warps: thin-plate splines and the decomposition of deformations," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 6, pp. 567-585, June 1989. doi: 10.1109/34.24792.
CRIS-Team Scopus Importer:2019-10-29

CraquelureNet: Matching The Crack Structure In Historical Paintings For Multi-Modal Image Registration - Sindel, Aline; Maier, Andreas; Christlein, Vincent (2021 IEEE International Conference on Image Processing (ICIP), 2021)
Visual light photography, infrared reflectography, ultraviolet fluorescence photography and x-radiography reveal even hidden compositional layers in paintings. To investigate the connections between these images, a multi-modal registration is essential. Due to varying image resolutions, modality dependent image content and depiction styles, registration poses a challenge. Historical paintings usually show crack structures called craquelure in the paint. Since craquelure is visible by all modalities, we extract craquelure features for our multi-modal registration method using a convolutional neural network. We jointly train our keypoint detector and descriptor using multi-task learning. We created a multi-modal dataset of historical paintings with keypoint pair annotations and class labels for craquelure detection and matching. Our method demonstrates the best registration performance on the multi-modal dataset in comparison to competing method
Keywords: Multi-modal registration; keypoint detection; descriptor learning; historical paintings; crack detection

Cross-Domain Metal Segmentation for CBCT Metal Artifact Reduction - Rohleder, Maximilian; Gottschalk, Tristan; Maier, Andreas; Kreher, Bjoern W. (Proceedings of SPIE - The International Society for Optical Engineering, 2022)
Metallic objects in the volume of a CBCT system can cause various artifacts after image reconstruction such as bright and dark streaks, local distortions of CT values and shadowing. In the intraoperative setting, this drastically reduces clinical value and can harden decision making. Most existing approaches to reduce such artifacts rely on a threshold-based metal segmentation in the reconstruction domain, which is prone to failure; especially in cases with extreme artifacts. Faulty metal masks impair these inpainting-based MAR methods and at times even worsen image quality by introducing secondary artifacts. In this work, a novel neural network topology is proposed to segment metallic objects in CBCT reconstruction domain by leveraging information of the given raw projection images. A reconstruction operator is embedded into this architecture, which enables the model to yield projection and reconstruction domain information during end-to-end training. This cross-domain approach is compared to the self-configuring segmentation method”nnUNet”, which predicts the three dimensional metal masks directly from the artifact corrupted reconstruction. To provide a baseline, a segmentation using a global dice-optimal threshold is determined. Segmentation results on simulated data confirmed by 5-fold cross validation show that the cross-domain network yields a mean dice coefficient of 0.87 ± 0.05 at a prediction time of 4s per volume. The reference method achieves 0.86 ± 0.03 in 43s, whereas the optimal similarity using a threshold averages to 0.45 ± 0.22.
CRIS-Team Scopus Importer:2022-11-25
Keywords: Cone-Beam CT; Known Operator Integration; Metal Artifacts; Segmentation

Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition - Hernandez, Abner; Pérez Toro, Paula Andrea; Nöth, Elmar; Orozco Arroyave, Juan Rafael; Maier, Andreas; Yang, Seung Hee (Proceedings of Interspeech 2022, 2022)
State-of-the-art automatic speech recognition (ASR) systems perform well on healthy speech. However, the performance on impaired speech still remains an issue. The current study explores the usefulness of using Wav2Vec self-supervised speech representations as features for training an ASR system for dysarthric speech. Dysarthric speech recognition is particularly difficult as several aspects of speech such as articulation, prosody and phonation can be impaired. Specifically, we train an acoustic model with features extracted from Wav2Vec, Hubert, and the cross-lingual XLSR model. Results suggest that speech representations pretrained on large unlabelled data can improve word error rate (WER) performance. In particular, features from the multilingual model led to lower WERs than Fbanks or models trained on a single language. Improvements were seen in English speakers with cerebral palsy caused dysarthria (UASpeech corpus), Spanish speakers with Parkinsonian dysarthria (PC-GITA corpus) and Italian speakers with paralysis-based dysarthria (EasyCall corpus). Compared to using Fbank features, XLSR-based features reduced WERs by 6.8%, 22.0%, and 7.0% for the UASpeech, PC-GITA, and EasyCall corpus, respectively.<br
Keywords: wav2vec; speech recognition; dysarthric speech; Parkinson’s disease; cerebral palsy

Cryo-Balloon Catheter Localization Based on a Support-Vector-Machine Approach - Kurzendorfer, Tanja; Mewes, Philip; Maier, Andreas; Strobel, Norbert; Brost, Alexander (IEEE Transactions on Medical Imaging, 2016)
Cryo-balloon catheters have attracted an increasing amount of interest in the medical community as they can reduce patient risk during left atrial pulmonary vein ablation procedures. As cryo-balloon catheters are not equipped with electrodes, they cannot be localized automatically by electro-anatomical mapping systems. As a consequence, X-ray fluoroscopy has remained an important means for guidance during the procedure. Most recently, image guidance methods for fluoroscopy-based procedures have been proposed, but they provide only limited support for cryo-balloon catheters and require significant user interaction. To improve this situation, we propose a novel method for automatic cryo-balloon catheter detection in fluoroscopic images by detecting the cryo-balloon catheter's built-in X-ray marker. Our approach is based on a blob detection algorithm to find possible X-ray marker candidates. Several of these candidates are then excluded using prior knowledge. For the remaining candidates, several catheter specific features are introduced. They are processed using a machine learning approach to arrive at the final X-ray marker position. Our method was evaluated on 75 biplane fluoroscopy images from 40 patients, from two sites, acquired with a biplane angiography system. The method yielded a success rate of 99.0% in plane A and 90.6% in plane B, respectively. The detection achieved an accuracy of 1.00mm ± 0.82mm in plane A and 1.13mm ± 0.24mm in plane B. The localization in 3-D was associated with an average error of 0.36mm ± 0.86mm.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.cryoba
Keywords: Ablation; atrial fibrillation; cryo-balloon; detection; electrophysiology; fluoroscopy; left atrium

CT Image Reconstruction Basics - Hornegger, Joachim; Maier, Andreas; Kowarschik, Markus (MR and CT Perfusion and Pharmacokinetic Imaging: Clinical Applications and Theoretical Principles, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.ctimag
Keywords: CT Reconstruction; Introduction

Cytologic scoring of equine exercise-induced pulmonary hemorrhage: Performance of human experts and a deep learning-based algorithm - Bertram, Christof A.; Marzahl, Christian; Bartel, Alexander; Stayt, Jason; Bonsembiante, Federico; Beeler-Marfisi, Janet; Barton, Ann K.; Brocca, Ginevra; Gelain, Maria E.; Glasel, Agnes; Du Preez, Kelly; Weiler, Kristina; et al. (Veterinary Pathology, 2022)
Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of annotators and to validate a deep learning-based algorithm for the THS. Digitized cytological specimens stained for iron were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotator’s and algorithmic performance included a ground truth dataset, the mean annotators’ THSs, and chemical iron measurements. Results of the study showed that annotators had marked interobserver variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared with the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of < or ≥ 75) of 75.7%, whereas, the algorithm had an accuracy of 92.3% with no relevant differences in correlation with chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis.
CRIS-Team Scopus Importer:2022-12-02
Keywords: artificial intelligence; automated image analysis; bronchoalveolar lavage fluid; computational pathology; digital pathology; equine; pulmonary hemorrhage; respiratory disease; total hemosiderin score

Data Completeness Estimation for 3D C-Arm Scans with Rotated Detector to Enlarge the Lateral Field-of-View - Stromer, Daniel; Kugler, Patrick; Bauer, Sebastian; Lauritsch, Günter; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.dataco_2
Keywords: Field-of-View Enlargement; Detector Rotation

Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior - Huang, Yixing; Preuhs, Alexander; Lauritsch, Guenter; Manhart, Michael; Huang, Xiaolin; Maier, Andreas (Machine Learning for Medical Image Reconstruction, 2019)
Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise.Thus, generating reconstructed images directly from a neural network appears inadequate. We propose to constrain the reconstructed images to be consistent with the measured projection data, while the unmeasured information is complemented by learning based methods. For this purpose, a data consistent artifact reduction (DCAR) method is introduced: First, a prior image is generated from an initial limited angle reconstruction via deep learning as a substitute for missing information. Afterwards, a conventional iterative reconstruction algorithm is applied, integrating the data consistency in the measured angular range and the prior information in the missing angular range. This ensures data integrity in the measured area, while inaccuracies incorporated by the deep learning prior lie only in areas where no information is acquired. The proposed DCAR method achieves significant image quality improvement: for 120-degree cone-beam limited angle tomography more than 10% RMSE reduction in noise-free case and more than 24% RMSE reduction in noisy case compared with a state-of-the-art U-Net based method.
Keywords: Deep learning; Limited angle tomography; Data consistency; Poisson noise; Robustness; Generalization ability

Data Driven Gating in SPECT using Isomap - Reymann, Maximilian; Vija, A. Hans; Maier, Andreas (Society of Nuclear Medicine and Molecular Imaging Annual Meeting 2023, 2023)

Data-Driven Speed-of-Sound Reconstruction for Medical Ultrasound: Impacts of Training Data Format and Imperfections on Convergence - Khun Jush, Farnaz; Dueppenbecker, Peter Michael; Maier, Andreas (Medical Image Understanding and Analysis, 2021)
B-mode imaging is a qualitative method and its interpretation depends on users' experience. Quantitative tissue information can increase precision and decrease user ambiguity. For example, Speed-of-Sound (SoS) in tissue is known to carry diagnostic information. Studies showed the possibility of SoS reconstruction from ultrasound raw data (a.k.a., RF data) using deep neural networks (DNNs). However, many ultrasound systems are designed to process demodulated data (i.e., IQ data) and often decimate data in early stages of acquisition. In this study we investigated the impacts of input data format and decimation on convergence of the DNNs for SoS reconstruction. Our results show that fully data-driven SoS reconstruction is possible using demodulated ultrasound data presented in Cartesian or Polar format using an encoder-decoder network. We performed a study using only amplitude and only phase information of ultrasound data for SoS reconstruction. Our results showed that distortion of the phase information results in inconsistent SoS predictions, indicating sensitivity of the investigated approach to phase information. We demonstrated that without losing significant accuracy, decimated IQ data can be used for SoS reconstruction.

Data Extrapolation from Learned Prior Images for Truncation Correction in Computed Tomography - Huang, Yixing; Preuhs, Alexander; Manhart, Michael; Lauritsch, Günter; Maier, Andreas (IEEE Transactions on Medical Imaging, 2021)
Data truncation is a common problem in computed tomography (CT). Truncation causes cupping artifacts inside the field-of-view (FOV) and anatomical structures missing outside the FOV. Deep learning has achieved impressive results in CT reconstruction from limited data. However, its robustness is still a concern for clinical applications. Although the image quality of learning-based compensation schemes may be inadequate for clinical diagnosis, they can provide prior information for more accurate extrapolation than conventional heuristic extrapolation methods. With extrapolated projection, a conventional image reconstruction algorithm can be applied to obtain a final reconstruction. In this work, a general plug-and-play (PnP) method for truncation correction is proposed based on this idea, where various deep learning methods and conventional reconstruction algorithms can be plugged in. Such a PnP method integrates data consistency for measured data and learned prior image information for truncated data. This shows to have better robustness and interpretability than deep learning only. To demonstrate the efficacy of the proposed PnP method, two state-of-the-art deep learning methods, FBPConvNet and Pix2pixGAN, are investigated for truncation correction in cone-beam CT in noise-free and noisy cases. Their robustness is evaluated by showing false negative and false positive lesion cases. With our proposed PnP method, false lesion structures are corrected for both deep learning methods. For FBPConvNet, the root-mean-square error (RMSE) inside the FOV can be improved from 92HU to around 30HU by PnP in the noisy case. Pix2pixGAN solely achieves better image quality than FBPConvNet solely for truncation correction in general. PnP further improves the RMSE inside the FOV from 42HU to around 27HU for Pix2pixGAN. The efficacy of PnP is also demonstrated on real clinical head data.<br
Keywords: Deep learning; robustness; interpretability; truncation correction; computed tomography

Dataset of pages from early printed books with multiple font groups - Seuret, Mathias; Limbach, Saskia; Weichselbaumer, Nikolaus; Maier, Andreas; Christlein, Vincent (ACM International Conference Proceeding Series, 2019)
Based on contemporary scripts, early printers developed a large variety of different fonts. While fonts may slightly differ from one printer to another, they can be divided into font groups, such as Textura, Antiqua, or Fraktur. The recognition of font groups is important for computer scientists to select adequate OCR models, and of high interest to humanities scholars studying early printed books and the history of fonts. In this paper, we introduce a new, public dataset for the recognition of font groups in early printed books, and evaluate several state-of-the-art CNNs for the font group recognition task. The dataset consists of more than 35 600 page images, each page showing up to five different font groups, of which ten are considered in this dataset.
CRIS-Team Scopus Importer:2019-11-19
Keywords: Antiqua; Bastarda; Book history; Dataset; Digital humanities; Document analysis; Fonts; Fraktur; Gotico-Antiqua; Greek; Hebrew; Historical documents; Incunabula; Italic; Neural network; Rotunda; Schwabacher; Textura; Type; Typography

Dataset on Bi- and Multi-nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors - Bertram, Christof A.; Donovan, Taryn A.; Tecilla, Marco; Bartenschlager, Florian; Fragoso, Marco; Wilm, Frauke; Marzahl, Christian; Breininger, Katharina; Maier, Andreas; Klopfleisch, Robert; Aubreville, Marc (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Decoupling Respiratory and Angular Variation in Rotational X-ray Scans Using a Prior Bilinear Model - Geimer, Tobias; Keall, Paul; Breininger, Katharina; Caillet, Vincent; Dunbar, Michelle; Bert, Christoph; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
Data-driven respiratory signal extraction from rotational X-ray scans is a challenge as angular effects overlap with respiration-induced change in the scene. In this paper, we use the linearity of the X-ray transform to propose a bilinear model based on a prior 4D scan to separate angular and respiratory variation. The bilinear estimation process is supported by a B-spline interpolation using prior knowledge about the trajectory angle. Consequently, extraction of respiratory features simplifies to a linear problem. Though the need for a prior 4D CT seems steep, our proposed use-case of driving a respiratory motion model in radiation therapy usually meets this requirement. We evaluate on DRRs of 5 patient 4D CTs in a leave-one-phase-out manner and achieve a mean estimation error of 3.01% in the gray values for unseen viewing angles. We further demonstrate suitability of the extracted weights to drive a motion model for treatments with a continuously rotating gantry.
CRIS-Team Scopus Importer:2019-06-21
Keywords: Bilinear model; Feature extraction; Motion model; Respiratory signal; X-ray projection

DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany - Wenninger, Marc; Maier, Andreas; Schmidt, Jochen (Scientific Data, 2021)
Real-world domestic electricity demand datasets are the key enabler for developing and evaluating machine learning algorithms that facilitate the analysis of demand attribution and usage behavior. Breaking down the electricity demand of domestic households is seen as the key technology for intelligent smart-grid management systems that seek an equilibrium of electricity supply and demand. For the purpose of comparable research, we publish DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany. The dataset contains recordings of 15 homes over a period of up to 3.5 years, wherein total 50 appliances have been recorded at a frequency of 1 Hz. Recorded appliances are of significance for load-shifting purposes such as dishwashers, washing machines and refrigerators. One home also includes three-phase mains readings that can be used for disaggregation tasks. Additionally, DEDDIAG contains manual ground truth event annotations for 14 appliances, that provide precise start and stop timestamps. Such annotations have not been published for any long-term electricity dataset we are aware of.
CRIS-Team Scopus Importer:2021-07-30

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images - Zhong, Xia; Amrehn, Mario; Ravikumar, Nishant; Chen, Shuqing; Strobel, Norbert; Birkhold, Annette; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Scientific Reports, 2021)
In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise linear transformation of a point cloud for the joint tasks of registration and segmentation. The actor network estimates a set of plausible actions and the value network aims to select the optimal action for the current observation. Point-wise features that comprise spatial positions (and surface normal vectors in the case of structured meshes), and their corresponding image features, are used to encode the observation and represent the underlying 3D volume. The actor and value networks are applied iteratively to estimate a sequence of transformations that enable accurate delineation of object boundaries. The proposed approach was extensively evaluated in both segmentation and registration tasks using a variety of challenging clinical datasets. Our method has fewer trainable parameters and lower computational complexity compared to the 3D U-Net, and it is independent of the volume resolution. We show that the proposed method is applicable to mono- and multi-modal segmentation tasks, achieving significant improvements over the state-of-the-art for the latter. The flexibility of the proposed framework is further demonstrated for a multi-modal registration application. As we learn to predict actions rather than a target, the proposed method is more robust compared to the 3D U-Net when dealing with previously unseen datasets, acquired using different protocols or modalities. As a result, the proposed method provides a promising multi-purpose segmentation and registration framework, particular in the context of image-guided interventions.
CRIS-Team Scopus Importer:2021-02-26

Deep autofocus with cone-beam CT consistency constraint - Preuhs, Alexander; Manhart, Michael; Roser, Philipp; Stimpel, Bernhard; Syben-Leisner, Christopher; Psychogios, Marios; Kowarschik, Markus; Maier, Andreas (Bildverarbeitung für die Medizin, 2020)

DeepCEST 7 T: Fast and homogeneous mapping of 7 T CEST MRI parameters and their uncertainty quantification - Hunger, Leonie; Rajput, Junaid Rasool; Klein, Kiril; Mennecke, Angelika; Fabian, Moritz; Schmidt, Manuel; Glang, Felix; Herz, Kai; Liebig, Patrick; Nagel, Armin Michael; Scheffler, Klaus; Dörfler, Arnd; et al. (Magnetic Resonance in Medicine, 2022)
Purpose In this work, we investigated the ability of neural networks to rapidly and robustly predict Lorentzian parameters of multi-pool CEST MRI spectra at 7 T with corresponding uncertainty maps to make them quickly and easily available for routine clinical use. Methods We developed a deepCEST 7 T approach that generates CEST contrasts from just 1 scan with robustness against B-1 inhomogeneities. The input data for a neural feed-forward network consisted of 7 T in vivo uncorrected Z-spectra of a single B-1 level, and a B-1 map. The 7 T raw data were acquired using a 3D snapshot gradient echo multiple interleaved mode saturation CEST sequence. These inputs were mapped voxel-wise to target data consisting of Lorentzian amplitudes generated conventionally by 5-pool Lorentzian fitting of normalized, denoised, B-0- and B-1-corrected Z-spectra. The deepCEST network was trained with Gaussian negative log-likelihood loss, providing an uncertainty quantification in addition to the Lorentzian amplitudes. Results The deepCEST 7 T network provides fast and accurate prediction of all Lorentzian parameters also when only a single B-1 level is used. The prediction was highly accurate with respect to the Lorentzian fit amplitudes, and both healthy tissues and hyperintensities in tumor areas are predicted with a low uncertainty. In corrupted cases, high uncertainty indicated wrong predictions reliably. Conclusion The proposed deepCEST 7 T approach reduces scan time by 50% to now 6:42 min, but still delivers both B-0- and B-1-corrected homogeneous CEST contrasts along with an uncertainty map, which can increase diagnostic confidence. Multiple accurate 7 T CEST contrasts are delivered within seconds.
CRIS-Team WoS Importer:2022-12-02

Deep Denoising for Hearing Aid Applications - Aubreville, Marc; Ehrensperger, Kai; Rosenkranz, T.; Graf, B.; Puder, H.; Maier, Andreas (2018 16TH INTERNATIONAL WORKSHOP ON ACOUSTIC SIGNAL ENHANCEMENT (IWAENC), 2018)
CRIS-Team WoS Importer:2019-02-27

DeepECG: Towards 3-D Continuous Cardiac MRI without ECG-Gating - Deep Learning based R-Wave Classification for Automated Cardiac Phase Binning - Hoppe, Elisabeth; Wetzl, Jens; Yoon, Seung Su; Schneider, Manuel; Stimpel, Bernhard; Preuhs, Alexander; Maier, Andreas (International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition, 2020)

DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio - Schröter, Hendrik; Escalante-B, Alberto N.; Rosenkranz, Tobias; Maier, Andreas (International Workshop on Acoustic Signal Enhancement (IWAENC 2022), 2022)
Deep learning-based speech enhancement has seen huge improvements and recently also expanded to full band audio (48 kHz). However, many approaches have a rather high computational complexity and require big temporal buffers for real time usage e.g. due to temporal convolutions or attention. Both make those approaches not feasible on embedded devices. This work further extends DeepFilterNet, which exploits harmonic structure of speech allowing for efficient speech enhancement (SE). Several optimizations in the training procedure, data augmentation, and network structure result in state-of-the-art SE performance while reducing the real-time factor to 0.04 on a notebook Core-i5 CPU. This makes the algorithm applicable to run on embedded devices in real-time. The DeepFilterNet framework can be obtained under an open source license. <br
Keywords: DeepFilterNet; speech enhancement; full-band; two-stage modeling

DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering - Schröter, Hendrik; Escalante-B, Alberto N.; Rosenkranz, Tobias; Maier, Andreas (ICASSP 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022)
Complex-valued processing has brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the process is based on a time-frequency (TF) mask which is applied to a noisy spectrogram, while complex masks (CM) are usually preferred over real-valued masks due to their ability to modify the phase. Recent work proposed to use a complex filter instead of a point-wise multiplication with a mask. This allows to incorporate information from previous and future time steps exploiting local correlations within each frequency band. In this work, we propose DeepFilterNet, a two stage speech enhancement framework utilizing deep filtering. First, we enhance the spectral envelope using ERB-scaled gains modeling the human frequency perception. The second stage employs deep filtering to enhance the periodic components of speech. Additionally to taking advantage of perceptual properties of speech, we enforce network sparsity via separable convolutions and extensive grouping in linear and recurrent layers to design a low complexity architecture. We further show that our two stage deep filtering approach outperforms complex masks over a variety of frequency resolutions and latencies and demonstrate convincing performance compared to other state-of-the-art models.
Keywords: deep filtering; speech enhancement

DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement - Schröter, Hendrik; Rosenkranz, Tobias; Escalante-B, Alberto N.; Maier, Andreas (INTERSPEECH 2023, 2023)
Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal. Deep Filtering (DF) was proposed to directly estimate a complex filter in frequency domain to take advantage of these correlations. In this work, we present a real-time speech enhancement demo using DeepFilterNet. DeepFilterNet's efficiency is enabled by exploiting domain knowledge of speech production and psychoacoustic perception. Our model is able to match state-of-the-art speech enhancement benchmarks while achieving a real-time-factor of 0.19 on a single threaded notebook CPU. The framework as well as pretrained weights have been published under an open source license.
Keywords: deep learning; speech enhancement; multi-frame filtering; deep filtering

Deep Generalized Max Pooling - Christlein, Vincent; Spranger, Lukas; Seuret, Mathias; Nikolaou, Angelos; Král, Pavel; Maier, Andreas (2019 International Conference on Document Analysis and Recognition (ICDAR), 2019)
Global pooling layers are an essential part of Convolutional Neural Networks (CNN). They are used to aggregate activations of spatial locations to produce a fixed-size vector in several state-of-the-art CNNs. Global average pooling or global max pooling are commonly used for converting convolutional features of variable size images to a fix-sized embedding. However, both pooling layer types are computed spatially independent: each individual activation map is pooled and thus activations of different locations are pooled together. In contrast, we propose Deep Generalized Max Pooling that balances the contribution of all activations of a spatially coherent region by re-weighting all descriptors so that the impact of frequent and rare ones is equalized. We show that this layer is superior to both average and max pooling on the classification of Latin medieval manuscripts (CLAMM'16, CLAMM'17), as well as writer identification (Historical-WI'17

Deep Geometric Supervision Improves Spatial Generalization in Orthopedic Surgery Planning - Kordon, Florian Johannes; Maier, Andreas; Swartman, Benedict; Privalov, Maxim; El Barbari, Jan Siad; Kunze, Holger (Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, 2022)
Careful surgical planning facilitates the precise and safe placement of implants and grafts in reconstructive orthopedics. Current attempts to (semi-)automatic planning separate the extraction of relevant anatomical structures on X-ray images and perform the actual positioning step using geometric post-processing. Such separation requires optimization of a proxy objective different from the actual planning target, limiting generalization to complex image impressions and the positioning accuracy that can be achieved. We address this problem by translating the geometric steps to a continuously differentiable function, enabling end-to-end gradient flow. Combining this companion objective function with the original proxy formulation improves target positioning directly while preserving the geometric relation of the underlying anatomical structures. We name this concept Deep Geometric Supervision. The developed method is evaluated for graft fixation site identification in medial patellofemoral ligament (MPFL) reconstruction surgery on (1) 221 diagnostic and (2) 89 intra-operative knee radiographs. Using the companion objective reduces the median Euclidean Distance error for MPFL insertion site localization from (1) 2.29mm" role="presentation">2.29mm to 1.58mm" role="presentation">1.58mm and (2) 8.70px" role="presentation">8.70px to 3.44px" role="presentation">3.44px, respectively. Furthermore, we empirically show that our method improves spatial generalization for strongly truncated anatom
CRIS-Team Scopus Importer:2022-10-14
Keywords: Surgical planning; Orthopedics; Landmark detection

Deep Image Denoising in SPECT - Reymann, Maximilian; Würfl, Tobias; Ritt, Philipp; Cachovan, Michal; Stimpel, Bernhard; Maier, Andreas (European Journal of Nuclear Medicine and Molecular Imaging (2019) 46 (Suppl 1): S1–S952, 10.1007/s00259-019-04486-2, 2019)
Aim/Introduction: Single Photon Emitted Computed Tomography (SPECT) images are characterized by a high degree of image noise, which is due to the low photon count at the detector. Recently, several deep learning-based approaches for image denoising have been proposed for low-dose Computed Tomography (CT). We investigate their suitability for SPECT denoising. Materials and Methods: In order to have a groundtruth for training the networks, a Monte Carlo Simulation was set up with the SIMIND software, where in total 24 SPECT acquisitions of brains, lungs, livers and skeleton were simulated with the XCAT phantom. Pairs of noisy and clean SPECT images were then used for training the neural networks. The networks tested were the U-Net as proposed by Heinrich et al. [1], the convolutional denoising autoencoder (CNN DAE) proposed by Gondara et al. [2] and a custom U-Net that had a depth of 4 layers with 2 convolutions, Batch Normalization and ReLU in each layer. Training of the networks was done with the hyperparameters as described in the original publications. Results: The best denoising performance of the neural networks was achieved by either the CNN DAE when measured with the Structural Similarity (SSIM) index of 0.933 or by the custom U-Net with a Peak Signal to Noise Ratio (PSNR) of 30.03 dB. This corresponded to a signal quality improvement of 0.09 compared to the noisy input when measured with the SSIM, or 5.9 dB when measured with the PSNR. The denoised SPECT images showed improved visual appearance, however, gain in image quality was not as distinct as on low-dose CT data. We assume this was due to the image characteristics of SPECT images, which are characterized by few distinct image values and a more heterogeneous pixel intensity distribution compared to CT data. Conclusion: Image characteristics in SPECT pose a special challenge to deep learning-based image denoising methods, as the signal is sparse and contains a high degree of noise. We believe this, in conjunction with the limited number of samples explains the weaker benefit in performance in SPECT when compared to low-dose CT data. We assume that with a more diverse training set and specialized loss function suitable for SPECT data , we can further increase performanc
Keywords: Deep Learning; SPECT; Denoising; Monte Carlo Simulation; XCAT; Phantom

Deep Iterative 2D/3D Registration - Jaganathan, Srikrishna; Wang, Jian; Borsdorf, Anja; Shetty, Karthik; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 2021)
Deep Learning-based 2D/3D registration methods are highly robust but often lack the necessary registration accuracy for clinical application. A refinement step using the classical optimization-based 2D/3D registration method applied in combination with Deep Learning-based techniques can provide the required accuracy. However, it also increases the runtime. In this work, we propose a novel Deep Learning driven 2D/3D registration framework that can be used end-to-end for iterative registration tasks without relying on any further refinement step. We accomplish this by learning the update step of the 2D/3D registration framework using Point-to-Plane Correspondences. The update step is learned using iterative residual refinement-based optical flow estimation, in combination with the Point-to-Plane correspondence solver embedded as a known operator. Our proposed method achieves an average runtime of around 8s, a mean re-projection distance error of 0.60±0.40 mm with a success ratio of 97% and a capture range of 60 mm. The combination of high registration accuracy, high robustness, and fast runtime makes our solution ideal for clinical applications.

Deep learning acceleration of Total Lagrangian Explicit Dynamics for soft tissue mechanics - Meister, Felix; Passerini, Tiziano; Mihalef, Viorel; Tuysuzoglu, Ahmet; Maier, Andreas; Mansi, Tommaso (Computer Methods in Applied Mechanics and Engineering, 2020)
Simulating complex soft tissue deformations has been an intense research area in the fields of computer graphics or computational physiology for instance. A desired property is the ability to perform fast, if not real-time, simulations while being physically accurate. Numerical schemes have been explored to speed up finite element methods, like the Total Lagrangian Explicit Dynamics (TLED). However, real-time applications still come at the price of accuracy and fidelity. In this work, we explore the use of neural networks as function approximators to accelerate the time integration of TLED, while being generic enough to handle various geometries, motion and materials without having to retrain the neural network model. The method is evaluated on a set of experiment, showing promising accuracy at time steps up to 20 times larger than the “breaking” time step, as well as in a simple medical application. Such an approach could pave the way to very fast but accurate acceleration strategies for computational biomechanics.
CRIS-Team Scopus Importer:2019-10-08
Keywords: Data-driven computational modeling; Deep learning; Finite element method; Soft tissue biomechanics; Total Lagrangian Explicit Dynamics

Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans - Denzinger, Felix; Wels, Michael; Breininger, Katharina; Reidelshöfer, Anika; Eckert, Joachim; Sühling, Michael; Schmermund, Axel; Maier, Andreas (BVM Workshop 2020, 2020)
Analysing coronary artery plaque segments with respect to their functional significance and therefore their influence to patient management in a non-invasive setup is an important subject of current research. In this work we compare and improve three deep learning algorithms for this task: A 3D recurrent convolutional neural network (RCNN), a 2D multi-view ensemble approach based on texture analysis, and a newly proposed 2.5D approach. Current state of the art methods utilising fluid dynamics based fractional flow reserve (FFR) simulation reach an AUC of up to 0.93 for the task of predicting an abnormal invasive FFR value. For the comparable task of predicting revascularisation decision, we are able to improve the performance in terms of AUC of both existing approaches with the proposed modifications, specifically from 0.80 to 0.90 for the 3D-RCNN, and from 0.85 to 0.90 for the multi-view texture-based ensemble. The newly proposed 2.5D approach achieves comparable results with an AUC of 0.90.<br

Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region - Aubreville, Marc; Bertram, Christof A.; Marzahl, Christian; Gurtner, Corinne; Dettwiler, Martina; Schmidt, Anja; Bartenschlager, Florian; Merz, Sophie; Fragoso, Marco; Kershaw, Olivia; Klopfleisch, Robert; Maier, Andreas (Scientific Reports, 2020)
Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section. We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide. Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963–0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count. To achieve better inter-rater agreement, we propose to use a computer-based area selection for support of the pathologist in the manual mitotic count.
CRIS-Team Scopus Importer:2020-10-16

Deep Learning-based Adaptive Image Combination for Signal-Dropout Suppression in Liver DWI - Gadjimuradov, Fasil; Benkert, Thomas; Nickel, Marcel Dominik; Maier, Andreas (ISMRM & SMRT Annual Conference & Exhibition, 2021)

Deep Learning-Based Anonymization of Chest Radiographs: A Utility-Preserving Measure for Patient Privacy - Packhäuser, Kai; Gündel, Sebastian; Thamm, Florian; Denzinger, Felix; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, 2023)

Deep Learning based Attribute Representation in Ancient Vase Paintings - Madhu, Prathmesh; Kosti, Ronak Vijaypal; Bendschus, Torsten; Reinhardt, Corinna; Bell, Peter; Christlein, Vincent; Maier, Andreas (Digital Humanities 2020 - Intersections/Carrefours, 2020)
The understanding of iconography and visual narration in ancient imagery is one of the main foci in the field of Classical Archaeology, e.g. in Attic vase paintings of the fifth century B.C. In order to depict the situations and actions of a narrative as well as to characterise its protagonists, ancient Greek artists made use of a broad variety of often similar image elements [1]. The interaction and meaningful relationship of the protagonists is depicted with significant postures and gestures (schemata) in order to illustrate key aspects of the storyline [2, 3]. These schemes are not restricted to a certain iconography, so that visual links between different images occur. Being familiar with these relationships the ancient viewer could detect the specific narration and understand the meaning of the image. For example, the scheme of leading the bride in Attic vase paintings is characterised by a significant leading-gesture (χεῖρ’ ἐπὶ καρπῷ – hand on wrist / hand on hand) that relates bride and bridegroom in non-mythological wedding scenes, thereby expressing a hierarchy of the two figures in active or passive parts respectively. Both protagonists are connected in a communicative way defined by meaningful postures and gestures. Besides these descriptive schemes there are narrative elements, like the surrounding, specific attributes of gods or other objects, that contribute to the viewer’s understanding of narrative contexts. These other characteristic attributes are needed in order to determine the depiction as a specific mythological one. Scenes of Helen and Menelaos (dressed as warrior and threatening her with weapons), thus, make use of the leading the bride scheme to show that Menelaos leads Helen home from Troy, like a bride. Only by combining the formal schemes and narrative elements is it possible to determine the depiction as a certain mythological story and to understand its cultural meaning. We, therefore, need to recognize the narrative elements of the pictorial stories and to decode their formal key structures by comparing a high number of ancient images and by discovering visual similarities. It is necessary to target the understanding of complex semantic relationships and cultural references in a mixed-initiative-interaction between researcher and machine, and involve computer vision as a fourth Bildwissenschaft [4]. We approach this problem using state-of-the-art object recognition algorithms [5, 6] in computer vision. We present a novel approach using convolutional neural networks which maps the attributes occurring in Classical Archaeology into a deep representational space, through deep learning techniques. This representation maps the inherent variations in the representation of any particular attribute into a common space. A further 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. We also analyse the model performance with abstract classes where we combine multiple similar attributes into a single abstract class. This new approach has important applications, including the retrieval and contextual understanding of artworks [7] in Classical Archaeology. References: [1] Giuliani, L., 2003. Bild und Mythos. Geschichte der Bilderzählung in der griechischen Kunst, München: Beck 2003. [2] McNiven, T. J., 1982. Gestures in Attic Vase Painting: Use and Meaning, 550–450 B.C., Diss. Ann Arbor: University of Michigan. [3] Catoni, M. L., 2008. La comunicazione non verbale nella Grecia antica : gli schemata nella danza, nell'arte, nella vita, Turin: Bollati Boringhieri. [4] Panofsky, E., 2006. Ikonographie und Ikonologie: Bildinterpretation nach dem Dreistufenmodell, Cologne: DuMont. [5] 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). [6] Redmon, J., Divvala, S., Girshick, R. and Farhadi, A., 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788). [7] Garcia, N., & Vogiatzis, G. (2019). How to read paintings: Semantic art understanding with multi-modal retrieval. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11130 LNCS, 676–691. https://doi.org/10.1007/978-3-030-11012-3_
Keywords: Vase Paintings; Machine Learning; Computer Vision

Deep Learning-Based Classification of Inflammatory Arthritis by Identification of Joint Shape Patterns—How Neural Networks Can Tell Us Where to “Deep Dive” Clinically - Folle, Lukas; Simon, David; Tascilar, Koray; Krönke, Gerhard; Liphardt, Anna-Maria; Maier, Andreas; Schett, Georg; Kleyer, Arnd (Frontiers in Medicine, 2022)
Keywords: artificial intelligence; arthritis; joint; bone; deep learning

Deep learning-based denoising of mammographic images using physics-driven data augmentation - Eckert, Dominik; Vesal, Sulaiman; Ritschl, Ludwig; Kappler, Steffen; Maier, Andreas (Informatik aktuell, 2020)
Mammography is using low-energy X-rays to screen the human breast and is utilized by radiologists to detect breast cancer. Typically radiologists require a mammogram with impeccable image quality for an accurate diagnosis. In this study, we propose a deep learning method based on Convolutional Neural Networks (CNNs) for mammogram denoising to improve the image quality. We first enhance the noise level and employ Anscombe Transformation (AT) to transform Poisson noise to white Gaussian noise. With this data augmentation, a deep residual network is trained to learn the noise map of the noisy images. We show, that the proposed method can remove not only simulated but also real noise. Furthermore, we also compare our results with state-of-the-art denoising methods, such as BM3D and DNCNN. In an early investigation, we achieved qualitatively better mammogram denoising results.
CRIS-Team Scopus Importer:2020-04-21

Deep learning based denoising of mammographic x-ray images: an investigation of loss functions and their detail-preserving properties - Eckert, Dominik; Ritschl, Ludwig; Herbst, Magdalena; Wicklein, Julia; Vesal, Sulaiman; Kappler, Steffen; Maier, Andreas; Stober, Sebastian (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2022)
Digital Breast Tomosynthesis (DBT) is becoming increasingly popular for breast cancer screening because of its high depth resolution. It uses a set of low-dose x-ray images called raw projections to reconstruct an arbitrary number of planes. These are typically used in further processing steps like backprojection to generate DBT slices or synthetic mammography images. Because of their low x-ray dose, a high amount of noise is present in the projections. In this study, the possibility of using deep learning for the removal of noise in raw projections is investigated. The impact of loss functions on the detail preservation is analized in particular. For that purpose, training data is augmented following the physics driven approach of Eckert et al.1 In this method, an x-ray dose reduction is simulated. First pixel intensities are converted to the number of photons at the detector. Secondly, Poisson noise is enhanced in the x-ray image by simulating a decrease in the mean photon arrival rate. The Anscombe Transformation2 is then applied to construct signal independent white Gaussian noise. The augmented data is then used to train a neural network to estimate the noise. For training several loss functions are considered including the mean square error (MSE), the structural similarity index (SSIM)3 and the perceptual loss.4 Furthermore the ReLU-Loss1 is investigated, which is especially designed for mammogram denoising and prevents the network from noise overestimation. The denoising performance is then compared with respect to the preservation of small microcalcifications. Based on our current measurements, we demonstrate that the ReLU-Loss in combination with SSIM improves the denoising results.
CRIS-Team Scopus Importer:2022-06-10
Keywords: Anscombe Transformation; Breast Cancer; DBT; Deep Learning; Denoising; Digital Breast Tomosynthesis; Loss Functions; Mammo-gram; X-Ray

Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images. - Aubreville, Marc; Stöve, Maike; Oetter, Nicolai; Goncalves, Miguel; Knipfer, Christian; Neumann, Helmut; Bohr, Christopher; Stelzle, Florian; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2019)
Probe-based confocal laser endomicroscopy (pCLE) is a subcellular in vivo imaging technique capable of producing images that enable diagnosis of malign structural modifications in epithelial tissue. Images acquired with pCLE are, however, often tainted by significant artifacts that impair diagnosis. This is especially detrimental for automated image analysis, which is why said images are often excluded from recognition pipelines. We present an approach for the automatic detection of motion artifacts in pCLE images and apply this methodology to a data set of 15 thousand images of epithelial tissue acquired in the oral cavity and the vocal folds. The approach is based on transfer learning from intermediate endpoints within a pre-trained Inception v3 network with tailored preprocessing. For detection within the non-rectangular pCLE images, we perform pooling within the activation maps of the network and evaluate this at different network depths. We achieved area under the ROC curve values of 0.92 with the proposed method, compared to 0.80 for the best feature-based machine learning approach. Our overall accuracy with the presented approach is 94.8%. Over traditional machine learning approaches with state-of-the-art features, we achieved significantly improved overall performance. PURPOSE METHODS RESULTS CONCLUSION

Deep learning-based ECG-free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging - Hoppe, Elisabeth; Wetzl, Jens; Yoon, Seung Su; Bacher, Mario; Roser, Philipp; Stimpel, Bernhard; Preuhs, Alexander; Maier, Andreas (IEEE Transactions on Medical Imaging, 2021)
For the clinical assessment of cardiac vitality, time-continuous tomographic imaging of the heart is used. To further detect e.g., pathological tissue, multiple imaging contrasts enable a thorough diagnosis using magnetic resonance imaging (MRI). For this purpose, timecontinous and multi-contrast imaging protocols were proposed. The acquired signals are binned using navigation approaches for a motion-resolved reconstruction. Mostly, external sensors such as electrocardiograms (ECG) are used for navigation, leading to additional workflow efforts. Recent sensor-free approaches are based on pipelines requiring prior knowledge, e.g., typical heart rates. We present a sensor-free, deep learning-based navigation that diminishes the need for manual feature engineering or the necessity of prior knowledge compared to previous works. A classifier is trained to estimate the R-wave timepoints in the scan directly from the imaging data. Our approach is evaluated on 3-D protocols for continuous cardiac MRI, acquired in-vivo and free-breathing with single or multiple imaging contrasts. We achieve an accuracy of >98% on previously unseen subjects, and a well comparable image quality with the state-of-the-art ECG-based reconstruction. Our method enables an ECG-free workflow for continuous cardiac scans with simultaneous anatomic and functional imaging with multiple contrasts. It can be potentially integrated without adapting the sampling scheme to other continuous sequences by using the imaging data for navigation and reconstructio

Deep Learning-based Extended Field of View Computed Tomography Image Reconstruction: Influence of Network Design on Image Estimation Outside the Scan Field of View - Singh Khural, Bhupinder; Baer-Beck, Matthias; Fournie, Eric; Stierstorfer, Karl; Huang, Yixing; Maier, Andreas (Biomedical Physics and Engineering Express, 2022)
The problem of data truncation in Computed Tomography (CT) is caused by the missing data when the patient exceeds the Scan Field of View (SFOV) of a CT scanner. The reconstruction of a truncated scan produces severe truncation artifacts both inside and outside the SFOV. We have employed a deep learning-based approach to extend the field of view and suppress truncation artifacts. Thereby, our aim is to generate a good estimate of the real patient data and not to provide a perfect and diagnostic image even in regions beyond the SFOV of the CT scanner. This estimate could then be used as an input to higher order reconstruction algorithms [1]. To evaluate the influence of the network structure and layout on the results, three convolutional neural networks (CNNs), in particular a general CNN called ConvNet, an autoencoder, and the U-Net architecture have been investigated in this paper. Additionally, the impact of L1, L2, structural dissimilarity and perceptual loss functions on the neural network’s learning have been assessed and evaluated. The evaluation of data set comprising 12 truncated test patients demonstrated that the U-Net in combination with the structural dissimilarity loss showed the best performance in terms of image restoration in regions beyond the SFOV of the CT scanner. Moreover, this network produced the best mean absolute error, L1, L2, and structural dissimilarity evaluation measures on the test set compared to other applied networks. Therefore, it is possible to achieve truncation artifact removal using deep learning techniques.<br
Keywords: computed tomography; convolutional neural network; U-Net; autoencoder; extended field of view; truncation artifacts

Deep learning based hybrid OCT-OCTA segmentation of Bruch's membrane in pathology - Schottenhamml, Julia; Ploner, Stefan; Moult, Eric M.; Duker, Jay S.; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2019)
CRIS-Team WoS Importer:2019-11-01

Deep Learning-based Inpainting for Virtual DSA - Unberath, Mathias; Hajek, Jonas; Geimer, Tobias; Schebesch, Frank; Amrehn, Mario; 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.deeple_3

Deep Learning Based Metal Inpainting in the Projection Domain: Initial Results - Gottschalk, Tristan; Kreher, Björn W.; Kunze, Holger; Maier, Andreas (Machine Learning for Medical Image Reconstruction, 2019)

Deep Learning Based Metal Inpainting in the Projection Domain using Additional Neighboring Projection Information - Gottschalk, Tristan; Kreher, Björn W.; Maier, Andreas (Proceedings of The 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020)

Deep Learning based Model Observers for Multi - Modal Imaging - Naderi Boldaji, Hamidreza; Patwari, Mayank; Reymann, Maximilian; Gutjahr, Ralf; Raupach, Rainer; Maier, Andreas (16th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2021)
Model Observers (MO) are useful tools for assessing task-based image quality in medical imaging systems. Detection and characterization of anatomical structures in clinical imaging are some of the most significant tasks of MOs. Various linear model observers have been widely applied for these tasks with great success. However, we are unable to deploy such observers without a high prior knowledge of the task, the signal, and the background. Additionally, most model observers currently used in medical imaging research are used to optimize single modality systems such as standalone CT or PET scanners. In this paper, we aim to solve these problem by means of a Convolutional Neural Network (CNN). We employ supervised learning with CNNs to approximate the Human Observer (HO) for a signal-known-exactly and background-known-statistically (SKE/BKS) signal detection task. We use this observer in a novel multi - modality imaging setting. First, we propose a CNN-based anthropomorphic model observer to predict human observer detection performance for PET images. Then, we suggest another CNN-based MO for multi - modal PET and CT images. We trained both networks using 160 sets of joint PET/CT images of a dataset from the MICCAI2020 HECKTOR challenge, and tested on 41 patients from the same dataset. Our CNN - MOs have high accuracy in detecting the lesions present in the image (AUC = 0.95). Furthermore, our MOs degrade monotonically with increase in noise levels, similar to how a HO would. Including both modalities into an MO results in improved lesion detection performance, especially in the presence of image noise, compared to single modality CNN - MOs.<br
Keywords: CNN; Deep Learning; Model Observers; CT; PET

Deep Learning-based Partial Fourier Reconstruction for Improved Prostate DWI - Gadjimuradov, Fasil; Yoon, Seung Su; Benkert, Thomas; Nickel, Marcel Dominik; Engelhard, Karl; Maier, Andreas (International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition, 2020)

Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data - Packhäuser, Kai; Gündel, Sebastian; Münster, Nicolas; Syben, Christopher; Christlein, Vincent; Maier, Andreas (Scientific Reports, 2022)
With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data contains sensitive patient-related information and is therefore usually anonymized by removing patient identifiers, e.g., patient names before publication. To the best of our knowledge, we are the first to show that a well-trained deep learning system is able to recover the patient identity from chest X-ray data. We demonstrate this using the publicly available large-scale ChestX-ray14 dataset, a collection of 112,120 frontal-view chest X-ray images from 30,805 unique patients. Our verification system is able to identify whether two frontal chest X-ray images are from the same person with an AUC of 0.9940 and a classification accuracy of 95.55%. We further highlight that the proposed system is able to reveal the same person even ten and more years after the initial scan. When pursuing a retrieval approach, we observe an mAP@R of 0.9748 and a precision@1 of 0.9963. Furthermore, we achieve an AUC of up to 0.9870 and a precision@1 of up to 0.9444 when evaluating our trained networks on external datasets such as CheXpert and the COVID-19 Image Data Collection. Based on this high identification rate, a potential attacker may leak patient-related information and additionally cross-reference images to obtain more information. Thus, there is a great risk of sensitive content falling into unauthorized hands or being disseminated against the will of the concerned patients. Especially during the COVID-19 pandemic, numerous chest X-ray datasets have been published to advance research. Therefore, such data may be vulnerable to potential attacks by deep learning-based re-identification algorithms.<br

Deep Learning-based Pipeline for Module Power Prediction from EL Measurements - Hoffmann, Mathis; Buerhop-Lutz, Claudia; Reeb, Luca; Pickel, Tobias; Winkler, Thilo; Doll, Bernd; Würfl, Tobias; Peters, Ian Marius; Brabec, Christoph; Maier, Andreas; Christlein, Vincent (Progress in Photovoltaics: Research and Applications, 2021)
Automated inspection plays an important role in monitoring large-scale photovoltaic power plants. Commonly, electroluminescense measurements are used to identify various types of defects on solar modules but have not been used to determine the power of a module. However, knowledge of the power at maximum power point is important as well, since drops in the power of a single module can affect the performance of an entire string. By now, this is commonly determined by measurements that require to discontact or even dismount the module, rendering a regular inspection of individual modules infeasible. In this work, we bridge the gap between electroluminescense measurements and the power determination of a module. We compile a large dataset of 719 electroluminescense measurementsof modules at various stages of degradation, especially cell cracks and fractures, and the corresponding power at maximum power point. Here,we focus on inactive regions and cracks as the predominant type of defect. We set up a baseline regression model to predict the power from electroluminescense measurements with a mean absolute error of 9.0+/-3.7W (4.0+/-8.4%). Then, we show that deep-learning can be used to train a model that performs significantly better (7.3+/-2.7W or 3.2+/-6.5%). With this work, we aim to open a new research topic. Therefore, we publicly release the dataset, the code and trained models to empower other researchers to compare against our results. Finally, we present a thorough evaluation of certain boundary conditions like the dataset size and an automated preprocessing pipeline for on-site measurements showing multiple modules at once.

Deep Learning based Prediction of Glioma Growth by using "Growing Neural Cellular Automata" - Huang, Yixing; Maier, Andreas; Sommer, Philipp; Fischer, Stefan; Srinivasan, Aishwarya Lakshmi; Fietkau, Rainer; Bert, Christoph; Putz, Florian (STRAHLENTHERAPIE UND ONKOLOGIE, 2022)
CRIS-Team WoS Importer:2022-06-03