06311 Abstracts Collection – Sensor Data and Information Fusion in Computer Vision and Medicine - Denzler, Joachim; Hornegger, Joachim; Kittler, Jürgen; Maurer Jr., Calvin R. (Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, 2007)
UnivIS-Import:2015-07-08:Pub.2007.tech.IMMD.IMMD5.06311a
Keywords: multi-sensor fusion; multi-modal perception; multiple expert fusion; fusion paradigms; multi-modal and intra-modal experts; non-rigid registration

2D-3D Non-rigid Registration using Iterative Reconstruction - Prümmer, Marcus; Han, Jingfeng; Hornegger, Joachim (Vision Modeling and Visualization, 2005)

2D/3D Registration for Motion Compensated Reconstruction in Cone-Beam CT of Knees Under Weight-Bearing Condition - Berger, Martin; Müller, Kerstin; Choi, Jang-Hwan; Aichert, André; Maier, Andreas; Fahrig, Rebecca (IFMBE Proceedings, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.2d3dre_8

2-D Interactive Scar Layer Visualization - Kurzendorfer, Tanja; Reiml, Sabrina; Brost, Alexander; Toth, Daniel; Panayiotou, Maria; Mountney, Peter; Steidl, Stefan; Maier, Andreas (IGIC 2017 - Abstract Book, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.2dinte

2D Respiration Navigation Framework for 3D Continuous Cardiac Magnetic Resonance Imaging - Hoppe, Elisabeth; Wetzl, Jens; Roser, Philipp; Felsner, Lina; Preuhs, Alexander; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

2D Vessel Segmentation Using Local Adaptive Contrast Enhancement - Schuldhaus, Dominik; Spiegel, Martin; Redel, Thomas; Polyanskaya, Maria; Struffert, Tobias; Hornegger, Joachim; Dörfler, Arnd (Bildverarbeitung für die Medizin, 2011)

3-D/2-D Registration of Left Atria Surface Models to Fluoroscopic Images for Cardiac Ablation Procedures - Weidler, Matthias (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2018)
Atrial fibrillation is one of the most common cardiac arrhythmias. It can be treated minimally invasive by catheter ablation. For guidance during the intervention, augmented fluoroscopy systems gain more and more popularity. These systems allow to fuse data which was pre-operatively acquired, e.g., using computed tomography, and intra-operative patient data. This facilitates navigation during the procedure by overlaying a 3-D model of the patient's left atrium to the fluoroscopic images. Moreover, if X-ray images are acquired from two views, 2-D image annotations can be displayed with respect to the 3-D patient model. Image fusion and annotation requires an accurate registration of pre-operative and intra-operative data which is mostly performed manually. This thesis is primarily concerned with methods for automatizing both the registration process and also steps which are required for registration. Thus, we propose also methods for reconstructing the 3-D shape of catheters from 2-D X-ray images as this is needed later for registration. In the first part of this thesis, we present methods for fast 3-D annotation of catheters. The first method is able to annotate whole line-shaped catheters in 2-D X-ray images based on a single seed point. To this end, catheter-like image regions are transformed into a graph like structure which serves as reduced search space for the catheter detection method. Resulting annotations in two X-ray images from different views can then be used to compute a 3-D reconstruction of the catheters. Our proposed method establishes point correspondences based on epipolar geometry. We define an optimality criterion that makes this approach robust with respect to spurious and missing point correspondences. Both methods are then used to establish a method for automatic cryoballoon catheter reconstruction. The second part investigates registration methods based on devices placed at certain anatomical structures. We present two different methods, one for thermal ablation and one for cryoablation. The first method relies on line-shaped devices placed outside the left atrium in the oesophagus and the coronary sinus. Their 3-D shape can be reconstructed using the algorithms presented in the first part and can then be aligned to their corresponding 3-D structures segmented from the preoperative data. The second method uses the pulmonary vein ostium which is a structure inside the left atrium in which cryoballoons are placed. A registration is established by relating the ostium position to a reconstruction of the cryoballoon computed using the approach presented in the first part. We use a skeletonization of the 3-D left atrium model to extract potential ostia from the model. In the last part we consider an automatic registration method based on injected contrast agent. In this context we present a method for classification of contrasted frames. Moreover, we present a novel similarity measure for 3-D/2-D registration that takes into account how plausible a registration is. Plausibility is determined with respect to a reconstructed contrast agent distribution within the 3-D left atrium and the contrast agent in the 2-D images. We show that a combination of this similarity measure and a measure that relates edge information from the contrast agent in 2-D images to edges of the 3-D model increases accuracy substantially. As a final step, the frame-wise registration results are postprocessed by means of a Markov chain model of the cardiac motion. This method of temporal filtering reduces outliers and improves registration quality significantly.<br
Keywords: Augmented Fluoroscopy

3D annotation and manipulation of medical anatomical structures - Vitanovski, Dime; Schaller, Christian; Hahn, Dieter; Daum, Volker; Hornegger, Joachim (Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2009)
Although the medical scanners are rapidly moving towards a three-dimensional paradigm, the manipulation and annotation/labeling of the acquired data is still performed in a standard 2D environment. Editing and annotation of three-dimensional medical structures is currently a complex task and rather time-consuming, as it is carried out in 2D projections of the original object. A major problem in 2D annotation is the depth ambiguity, which requires 3D landmarks to be identified and localized in at least two of the cutting planes. Operating directly in a three-dimensional space enables the implicit consideration of the full 3D local context, which significantly increases accuracy and speed. A three-dimensional environment is as well more natural optimizing the user's comfort and acceptance. The 3D annotation environment requires the three-dimensional manipulation device and display. By means of two novel and advanced technologies, Wii Nintendo Controller and Philips 3D WoWvx display, we define an appropriate 3D annotation tool and a suitable 3D visualization monitor. We define noncoplanar setting of four Infrared LEDs with a known and exact position, which are tracked by the Wii and from which we compute the pose of the device by applying a standard pose estimation algorithm. The novel 3D renderer developed by Philips uses either the Z-value of a 3D volume, or it computes the depth information out of a 2D image, to provide a real 3D experience without having some special glasses. Within this paper we present a new framework for manipulation and annotation of medical landmarks directly in three-dimensional volume. © 2009 SPIE.

3D Deep Learning-based Boundary Regression of an Age-related Retinal Biomarker in High Resolution OCT - Karbole, Wenke; Ploner, Stefan; Won, Jungeun; Marmalidou, Anna; Takahashi, Hiroyuki; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2024)
Vision is essential for quality of life, but is threatened by visionimpairing diseases like age-related macular degeneration (AMD). A recently proposed biomarker potentially to distinguish normal aging from AMD is the gap visualized between the retinal pigment epithelium (RPE) and the Bruch’s membrane. Due to lack of automated processing, to date, this gap was only described sparsely in histologic data or on optical coherence tomography (OCT) B-scans. By segmenting the posterior RPE boundary automatically for the first time, we enable fully-automatic quantification of the thickness of this gap in vivo across whole volumetric OCT images. Our novel processing pipeline leverages advancements in motion correction, volumetric image merging, and high resolution OCT. A novel 3D boundary regression network named depth map regression network (DMR-Net) estimates the gap thickness in the volume. As 3D networks require full-volume ground truth boundary labels, which are labor-intensive, we developed a novel semi-automatic labeling approach to refine existing labels based on the visibility of the gap with minimal user input. We demonstrate thickness maps across a wide age range of healthy participants (23 – 79 years). The median absolute error in the test set is 0.161 μm, which is well below the axial pixel spacing (0.89 μm). For the first time, our results allow spatially resolved analysis to investigate pathologic deviations in normal aging and AMD.
CRIS-Team Scopus Importer:2024-03-29

3D-Funktionalität und Navigation durch einen Siemens-Prototypen in der biplanen Fluoroskopie zur Pulmonalvenenisolation - Bourier, Felix; Heißenhuber, Frank; Schneider, Hans-Jürgen; Ganslmeier, Patrycja; Fischer, Robert; Brost, Alexander; Koch, Martin; Strobel, Norbert; Hornegger, Joachim; Kurzidim, Klaus (77. Jahrestagung, 2011)

3-D gesture-based scene navigation in medical imaging applications using Time-of-Flight cameras - Soutschek, Stefan; Penne, Jochen; Hornegger, Joachim; Kornhuber, Johannes (2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops, 2008)
For a lot of applications, and particularly for medical intra-operative applications, the exploration of and navigation through 3-D image data provided by sensors like ToF (Time-of-Flight) cameras, MUSTOF (Multisensor-Time-of- Flight) endoscopes or CT (Computed Tomography)[8], requires a user-interface which avoids physical interaction with an input device. Thus, we process a touchless userinterface based on gestures classified by the data provided by a ToF camera. Reasonable and necessary user interactions are described. For those interactions a suitable set of gestures is introduced. A user-interface is then proposed, which interprets the current gesture and performs the assigned functionality. For evaluating the quality of the developed user-interface we considered the aspects of classification rate, real-time applicability, usability, intuitiveness and training lime. The results of our evaluation show that our system, which provides a classification rate of 94.3% at a framerate of 11 frames per second, satisfactorily addresses all these quality requirements. © 2008 IEEE.

3-D Imaging of Coronary Vessels Using C-arm CT - Schwemmer, Chris (Logos Verlag Berlin, 2019)
Cardiovascular disease has become the number one cause of death worldwide. Its prevention, diagnosis and therapy are therefore highly important topics in today’s clinical practice and research. For the diagnosis and therapy of coronary artery disease, interventional C-arm-based fluoroscopy is an imaging method of choice. It delivers 2-D X-ray images from almost arbitrary directions, but a 2-D projection image is naturally limited in its depiction of complex spatial relations. While the C- arm systems are capable of rotating around the patient and thus allow a CT-like 3-D image reconstruction, their long rotation time of about five seconds leads to strong motion artefacts in 3-D coronary artery imaging. Several methods to estimate the coronary motion and compensate for it during 3-D image reconstruction can be found in the literature. All have their specific properties, advantages and disadvantages, which are discussed in the first part of this thesis. Then, a novel method is introduced that is based on a 2-D–2-D image registra- tion algorithm, henceforth called rmc (Registration-based Motion Compensation). It is embedded in an iterative algorithm for motion estimation and compensation. rmc does not require any complex segmentation or user interaction and is thus fully automatic, which is a very desirable feature for interventional applications. Motion estimation and compensation becomes more difficult when projection data from the whole heart cycle is used from the beginning. rmc overcomes this by successively increasing the utilised amount of projections in a bootstrapping process. Throughout the remainder of this thesis, rmc is first evaluated in a simulation study using a simple numerical phantom, then on the publicly available Cavarev platform (employing an anthropomorphic phantom), and finally in a study using 58 human clinical datasets. Through the simulation study, approximations for the inherent error of the investigated algorithms were established. In addition, evidence that the missing depth information of a 2-D motion model is not a limiting factor for coronary artery imaging was found. The Cavarev experiments investigated the effect of different filter kernel choices during the execution of rmc. For the quantitative evaluation on human clinical data, a new software called CoroEval was introduced to the scientific community. Overall, it could be shown from both the quantitative results as well as the human observer ratings that rmc can be successfully applied to a large set of clinical data without user interaction or parameter changes, and with a high robustness against initial 3-D image quality, while delivering results that are at least up to the current state of the art, and better in many cases.
UnivIS-Import:2019-08-15:Pub.2019.tech.IMMD.IMMD5.3dimag
Keywords: Cardiac imaging; C-arm CT; Coronary imaging; Motion estimation; Reconstruction

3-D Imaging of Coronary Vessels Using C-arm CT - Schwemmer, Chris (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Cardiovascular disease has become the number one cause of death worldwide. Its prevention, diagnosis and therapy are therefore highly important topics in today’s clinical practice and research. For the diagnosis and therapy of coronary artery disease, interventional C-arm-based fluoroscopy is an imaging method of choice. It delivers 2-D X-ray images from almost arbitrary directions, but a 2-D projection image is naturally limited in its depiction of complex spatial relations. While the C- arm systems are capable of rotating around the patient and thus allow a CT-like 3-D image reconstruction, their long rotation time of about five seconds leads to strong motion artefacts in 3-D coronary artery imaging. Several methods to estimate the coronary motion and compensate for it during 3-D image reconstruction can be found in the literature. All have their specific properties, advantages and disadvantages, which are discussed in the first part of this thesis. Then, a novel method is introduced that is based on a 2-D–2-D image registra- tion algorithm, henceforth called rmc (Registration-based Motion Compensation). It is embedded in an iterative algorithm for motion estimation and compensation. rmc does not require any complex segmentation or user interaction and is thus fully automatic, which is a very desirable feature for interventional applications. Motion estimation and compensation becomes more difficult when projection data from the whole heart cycle is used from the beginning. rmc overcomes this by successively increasing the utilised amount of projections in a bootstrapping process. Throughout the remainder of this thesis, rmc is first evaluated in a simulation study using a simple numerical phantom, then on the publicly available Cavarev platform (employing an anthropomorphic phantom), and finally in a study using 58 human clinical datasets. Through the simulation study, approximations for the inherent error of the investigated algorithms were established. In addition, evidence that the missing depth information of a 2-D motion model is not a limiting factor for coronary artery imaging was found. The Cavarev experiments investigated the effect of different filter kernel choices during the execution of rmc. For the quantitative evaluation on human clinical data, a new software called CoroEval was introduced to the scientific community. Overall, it could be shown from both the quantitative results as well as the human observer ratings that rmc can be successfully applied to a large set of clinical data without user interaction or parameter changes, and with a high robustness against initial 3-D image quality, while delivering results that are at least up to the current state of the art, and better in many case

3D imaging of myocardial perfusion and coronary tree morphology from a single rotational angiogram - Lauritsch, Günter; Rohkohl, Christopher; Hornegger, Joachim; Sinha, Anil-Martin; Rittger, Harald; Brachmann, Johannes; Rieber, Johannes; Rittger, Harald (Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 2011)
Diagnosis and treatment of coronary heart disease are performed in the catheter laboratory using an angiographic X-ray C-arm system. The morphology of the coronary tree and potentially ischemic lesions are determined in 2D projection views. The hemodynamic impact of the lesion would be valuable information for treatment decision. Using other modalities for functional imaging is disrupting the clinical workflow since the patient has to be transferred from the catheter laboratory to another scanner, and back to the catheter laboratory for performing the treatment. In this work a novel technology is used for simultaneous 3D imaging of first pass perfusion and the morphology of the coronary tree from a single rotational angiogram. A selective, single shot of contrast agent of less than 20ml directly into the coronaries is sufficient for a proper contrast resolution. Due to the long acquisition time cardiac motion has to be considered. A novel reconstruction technique for estimation and compensation of cardiac motion from the acquired projection data is used. The overlay of the 3D structure of the coronary tree and the perfusion image shows the correlation of myocardial areas and the associated coronary sections supporting that region. In a case example scar lesions caused by a former myocardial infarct are investigated. A first pass perfusion defect is found which is validated by a late enhancement magnetic resonance image. No ischemic defects are found. The non vital regions are still supported by the coronary vasculature. © 2011 SPIE.

3-D Imaging of the Heart Chambers with C-arm CT - Müller, Kerstin (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014)
Nowadays, angiography is the gold standard for the visualization of the morphology of the cardiac vasculature and cardiac chambers in the interventional suite. Up to now, high resolution 2-D X-ray images are acquired with a C-arm system in standard views and the diagnosis of the cardiologist is based on the observations in the planar X-ray images. No dynamic analysis of the cardiac chambers can be performed in 3-D. In the last years, cardiac imaging in 3-D using a C-arm system becomes of more and more interest in the interventional catheter laboratory. Furthermore, the analysis of the 3-D motion would provide valuable information with respect to functional cardiac imaging. However, cardiac motion is a challenging problem in 3-D imaging, which leads to severe imaging artifacts in the 3-D image. Therefore, the main research goal of this thesis was the visualization and extraction of dynamic and functional parameters of the cardiac chambers in 3-D using an interventional angiographic C-arm system. In this thesis, two different approaches for cardiac chamber motion-compensated reconstruction have been developed and evaluated. The first technique addresses the visualization of the left ventricle. Therefore, a whole framework for left ventricular tomographic reconstruction and wall motion analysis has been developed. Dynamic surface models are generated from the 2-D X-ray images acquired during a short scan of a C-arm scanner using the 2-D bloodpool information. The acquisition time is about 5 s and the patients have normal sinus rhythm. Due to the acquisition time of about 5 s of the C-arm, no valuable retrospective ECG-gated reconstructions are possible. The dynamic surface LV model comprises a sparse motion vector field on the surface, which can be used for functional wall motion analysis. Furthermore, applying various interpolation schemes, dense motion vector fields can be generated for a tomographic motion-compensated reconstruction. In this thesis, linear interpolation methods and spline-based methods have been compared. The combination of the wall motion analysis and the motion-compensated reconstruction is of great value to the diagnostic of pathological regions in cardiac interventions. The second motion-compensated reconstruction approach uses volume-based motion estimation algorithms for the reconstruction of two - left atrium and left ventricle - to four heart chambers. A longer C-arm acquisition and contrast protocol allows for the generation of initial images at various heart phases. However, the initial image quality is not sufficient for motion estimation. Therefore, different pre-processing techniques, e.g., bilateral filtering or iterative reconstruction techniques, to improve the image quality were tested in combination with different motion estimation techniques. Overall, the results of this thesis highly demonstrate the feasibility of dynamic and functional cardiac chamber imaging using data from an interventional angiographic C-arm system for clinical applications.<br
UnivIS-Import:2016-07-26:Pub.2014.tech.IMMD.IMMD5.3dimag
Keywords: cardiac C-arm CT; cardiac imaging; motion compensation; motion correction; motion estimation

3-D LGE-MRI Segmentation using a Random Forest Classifier and Dynamic Programming - Kurzendorfer, Tanja; Brost, Alexander; Forman, Christoph; Schmidt, Michaela; Tillmanns, Christoph; Steidl, Stefan; Maier, Andreas (ESMRMB 2017, 34th Annual Scientific Meeting, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.3dlgem

3D model-based catheter tracking for motion compensation in EP procedures - Brost, Alexander; Liao, Rui; Hornegger, Joachim; Strobel, Norbert (Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, 2010)

3-D OCT Motion Correction Efficiently Enhanced with OCT Angiography - Ploner, Stefan; Kraus, Martin F.; Husvogt, Lennart; Moult, Eric M.; Alibhai, A. Yasin; Schottenhamml, Julia; Geimer, Tobias; Rebhun, Carl B.; Lee, Byungkun; Baumal, Caroline R.; Waheed, Nadia K.; Duker, Jay S.; et al. (Investigative Ophthalmology & Visual Science, 2018)

3-D Operation Situs Reconstruction with Time-of-Flight Satellite Cameras Using Photogeometric Data Fusion - Haase, Sven; Bauer, Sebastian; Wasza, Jakob; Kilgus, Thomas; Maier-Hein, Lena; Schneider, Armin; Kranzfelder, Michael; Feußner, Hubertus; Hornegger, Joachim (Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013, Lecture Notes in Computer Science, 2013)

3-D Printing Based Production of Head and Neck Masks for Radiation Therapy Using Ct Volume Data: A Fully Automatic Framework - Chen, Shuqing; Lu, Yanye; Hopfgartner, Christian; Suehling, Michael; Steidl, Stefan; Hornegger, Joachim; Maier, Andreas (Proceedings of the 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.3dprin
Keywords: 3-D printing; immobilization mask; atlas registration

3D-Reconstruction in the Presence of Motion - Prümmer, Marcus; Hornegger, Joachim (Proceedings of the 4th Russian-Bavarian Conference on Biomedical Engineering, 2008)

3-D Reconstruction of Historical Documents using an X-Ray C-Arm CT System - Stromer, Daniel; Christlein, Vincent; Anton, Gisela; Kugler, Patrick; Maier, Andreas (Proceedings of the 31th conference on Image and Vision Computing New Zealand 2016, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.3dreco_2
Keywords: X-Ray imaging; Iron gall ink reconstruction; Historical Document Analysis

3D-Reconstruction of Stiff Wires from a Single Monoplane X-Ray Image - Breininger, Katharina; Hanika, Moritz; Weule, Mareike; Kowarschik, Markus; Pfister, Marcus; Maier, Andreas (Informatik aktuell, 2019)
of preoperative data with intraoperative fluoroscopic images has been shown to reduce contrast agent, radiation dose and procedure time during endovascular repair of aortic aneurysms. However, the quality of the fusion may deteriorate due to often severe deformations of the vasculature caused by instruments such as stiff wires. To adapt the preoperative information intraoperatively to these deformations, the 3D positions of the inserted instruments are required. In this work, we propose a reconstruction method for stiff wires that requires only a single monoplane acquisition, keeping the impact on the clinical workflow to a minimum. To this end, the wire is segmented in the available X-ray image. To allow for a reconstruction in 3D, we then estimate a virtual second view of the wire orthogonal to the real projection based on vessel centerlines from a preoperative computed tomography. Using the real and estimated wire positions, we reconstruct the catheter using epipolar geometry. We achieve a mean modified Hausdorff distance of 4.1mm between the 3D reconstruction and the true wire course.
CRIS-Team Scopus Importer:2019-05-14

3-D-Reconstruction of the Sphenoidal Sinus from Monocular Endoscopic Views: First results - Wittenberg, Thomas; Winter, Christian; Rupp, Stephan; Scholz, Ingo; Stamminger, Marc; Bumm, Klaus; Nimsky, Christopher (Proceedings BMT 2006, 2006)

3-D respiratory motion compensation during EP procedures by image-based 3-D lasso catheter model generation and tracking - Brost, Alexander; Liao, Rui; Hornegger, Joachim; Strobel, Norbert (Springer-verlag, 2009)
Radio-frequency catheter ablation of the pulmonary veins attached to the left atrium is usually carried out under fluoroscopy guidance. Two-dimensional X-ray navigation may involve overlay images derived from a static pre-operative 3-D volumetric data set to add anatomical details. However, respiratory motion may impair the utility of static overlay images for catheter navigation. We developed a system for image-based 3-D motion estimation and compensation as a solution to this problem for which no previous solution is yet known. It is based on 3-D catheter tracking involving 2-D/3-D registration. A biplane X-ray C-arm system is used to image a special circumferential (lasso) catheter from two directions. In the first step of the method, a 3-D model of the device is reconstructed. 3-D respiratory motion at the site of ablation is then estimated by tracking the reconstructed model in 3-D from biplane fluoroscopy. In our experiments, the circumferential catheter was tracked in 231 biplane fluoro frames (462 monoplane fluoro frames) with an average 2-D tracking error of 1.0 mm ± 0.5 mm. © 2009 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD5.3dresp

3D surface reconstruction for laparoscopic computer-assisted interventions: Comparison of state-of-the-art methods - Groch, A.; Seitel, A.; Hempel, Sarah-Marie; Speidel, S.; Engelbrecht, Rainer; Penne, Jochen; Höller, Kurt Emmerich; Röhl, S.; Yung, K.; Bodenstedt, S.; Pflaum, Felix; Dos Santos, T. R.; et al. (Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 2011)
One of the main challenges related to computer-assisted laparoscopic surgery is the accurate registration of pre-operative planning images with patient's anatomy. One popular approach for achieving this involves intraoperative 3D reconstruction of the target organ's surface with methods based on multiple view geometry. The latter, however, require robust and fast algorithms for establishing correspondences between multiple images of the same scene. Recently, the first endoscope based on Time-of-Flight (ToF) camera technique was introduced. It generates dense range images with high update rates by continuously measuring the run-time of intensity modulated light. While this approach yielded promising results in initial experiments, the endoscopic ToF camera has not yet been evaluated in the context of related work. The aim of this paper was therefore to compare its performance with different state-of-the-art surface reconstruction methods on identical objects. For this purpose, surface data from a set of porcine organs as well as organ phantoms was acquired with four different cameras: a novel Time-of-Flight (ToF) endoscope, a standard ToF camera, a stereoscope, and a High Definition Television (HDTV) endoscope. The resulting reconstructed partial organ surfaces were then compared to corresponding ground truth shapes extracted from computed tomography (CT) data using a set of local and global distance metrics. The evaluation suggests that the ToF technique has high potential as means for intraoperative endoscopic surface registration. © 2011 SPIE.

3D Tele-Medical Speech Therapy using Time-of-Flight Technology - Stürmer, Michael; Maier, Andreas; Schaller, Christian; Soutschek, Stefan; Penne, Jochen; Handschu, Rene; Scibor, Mateusz; Nöth, Elmar (4th European Congress for Medical and Biomedical Engineering, 2009)
Tele-medical applications allow the treatment of patients over long distances. This is of special importance in speech therapy, since speech therapists often operate in specialized centers for certain disorders. In this paper we propose a novel therapy system which employs Time-of-Flight (ToF) 3D cameras that acquire 3D surface data in real time. The distance data is compressed within a MPEG stream and transmitted to a remote client where a 3D visualization is implemented. The loss in accuracy compared to the original data is within 1.6 mm and 9.7 mm. The system can be used for real-time streaming.
Keywords: Speech Therapy; Time-of-Flight; Telemedicine; MPEG

3D Tensor Reconstruction in X-Ray Dark-Field Tomography: The First Phantom Result - Hu, Shiyang; Riess, Christian; Hornegger, Joachim; Fischer, Peter; Bayer, Florian; Weber, Thomas; Anton, Gisela; Maier, Andreas (Bildverarbeitung für die Medizin 2015, 2015)

3D-Visualisation of Cryo-Balloon Ablation Catheters to Evaluate Left Atrial Anatomy for PVI - Bourier, Felix; Brost, Alexander; Kleinöder, Andreas; Schneider, Hans-Jürgen; Heißenhuber, Frank; Ganslmeier, Patrycja; Raab, Jens; Koch, Martin; Hornegger, Joachim; Strobel, Norbert; Kurzidim, Klaus (Venice Arrhythmias 2011, 2011)

3D-Visualisation of Cryo-balloon ablation catheters to evaluate their matching with left atrial anatomy to improve Pulmonary Vein Isolation - Bourier, Felix; Brost, Alexander; Kleinöder, Andreas; Koch, Martin; Hornegger, Joachim; Strobel, Norbert; Schneider, Hans-Jürgen; Kurzidim, Klaus (28th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology, 2011)

3D-Visualisierung von Cryo Ballon Kathetern und linkem Atrium in der biplanen Fluoroskopie zur Pulmonalvenenisolation - Bourier, Felix; Vukajlovic, Dejan; Brost, Alexander; Hornegger, Joachim; Strobel, Norbert; Kurzidim, Klaus (78. Jahrestagung der Deutschen Gesellschaft für Kardiologie, 2012)

3D-Visualization of Cryo Balloon Catheters and the Left Atrium in Biplane Fluoroscopy to support Pulmonary Vein Isolation - Bourier, Felix; Vukajlovic, Dejan; Schneider, Hans-Jürgen; Brost, Alexander; Strobel, Norbert; Hornegger, Joachim; Kurzidim, Klaus (33rd Annual Scientific Meeting, 2012)

3rd Russian-Bavarian Conference on Biomedical Engineering - Hornegger, Joachim; Mayr, Ernst; Schookin, Sergey; Feußner, Hubertus; Navab, Nassir; Gulyaev, Yuri V.; Höller, Kurt Emmerich; Ganzha, Victor (Union aktuell, 2007)
UnivIS-Import:2015-07-08:Pub.2007.tech.IMMD.IMMD5.3rdrus
Keywords: Biomedical; Engineering; Recognition; NOTES; MUSTOF; Interdisciplinary

4D-CT Reconstrucion Using Sparsity Level Constrained Compressed Sensing - Wu, Haibo; Maier, Andreas; Hofmann, Hannes; Fahrig, Rebecca; Hornegger, Joachim (Proceedings of the second international conference on image formation in x-ray computed tomography, 2012)

4D DSA Iterative Reconstruction - Endres, Jürgen; Rohkohl, Christopher; Schafer, Sebastian; Royalty, Kevin; Maier, Andreas; Kowarschik, Markus; Hornegger, Joachim (Proceedings of the Fully3D, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.4ddsai

4D dynamic imaging of the eye using ultrahigh speed SS-OCT - Liu, Jonathan J.; Grulkowski, Ireneusz; Potsaid, Benjamin; Jayaraman, Vijaysekhar; Cable, Alex E.; Kraus, Martin; Hornegger, Joachim; Duker, Jay S.; Fujimoto, James G. (Proc. SPIE 8567, 2013)

4-D motion animation of coronary arteries from rotational angiography - Holub, Wolfgang; Rohkohl, Christopher; Schuldhaus, Dominik; Prümmer, Marcus; Lauritsch, Günter; Hornegger, Joachim (Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 2011)
Time-resolved 3-D imaging of the heart is a major research topic in the medical imaging community. Recent advances in the interventional cardiac 3-D imaging from rotational angiography (C-arm CT) are now also making 4-D imaging feasible during procedures in the catheter laboratory. State-of-the-art reconstruction algorithms try to estimate the cardiac motion and utilize the motion field to enhance the reconstruction of a stable cardiac phase (diastole). The available data offers a handful of opportunities during interventional procedures, e.g. the ECG-synchronized dynamic roadmapping or the computation and analysis of functional parameters. In this paper we will demonstrate that the motion vector field (MVF) that is output by motion compensated image reconstruction algorithms is in general not directly usable for animation and motion analysis. Dependent on the algorithm different defects are investigated. A primary issue is that the MVF needs to be inverted, i.e. the wrong direction of motion is provided. A second major issue is the non-periodicity of cardiac motion. In algorithms which compute a non-periodic motion field from a single rotation the in depth motion information along viewing direction is missing, since this cannot be measured in the projections. As a result, while the MVF improves reconstruction quality, it is insufficient for motion animation and analysis. We propose an algorithm to solve both problems, i.e. inversion and missing in-depth information in a unified framework. A periodic version of the MVF is approximated. The task is formulated as a linear optimization problem where a parametric smooth motion model based on B-splines is estimated from the MVF. It is shown that the problem can be solved using a sparse QR factorization within a clinical feasible time of less than one minute. In a phantom experiment using the publicly available CAVAREV platform, the average quality of a non-periodic animation could be increased by 39% by applying the proposed periodization and inversion method. © 2011 SPIE.

4-D Motion Field Estimation by Combined Multiple Heart Phase Registration (CMHPR) for Cardiac C-arm Data - Müller, Kerstin; Rohkohl, Christopher; Lauritsch, Günter; Schwemmer, Chris; Heidbüchel, Hein; De Buck, Stijn; Nuyens, Dieter; Kyriakou, Yiannis; Köhler, Christoph; Hornegger, Joachim (2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2012)
In interventional cardiology three-dimensionalanatomical and functional information of the cardiac chamberswould have an important impact on diagnosis and therapy. Withthe technology of C-arm CT it is possible to reconstruct intraprocedural3-D images from angiographic projection data. Inorder to generate accurate and artifact-free reconstructions fromdynamic cardiac projections, the motion needs to be taken intoaccount. We present the novel Combined Multiple Heart PhaseRegistration (CMHPR) method. CMHPR is an iterative motionestimation and compensation algorithm that uses projection dataacquired during a single C-arm sweep. Filtered-backprojection(FBP) volumes from electrocardiogram (ECG)-gated data are reconstructedfor different motion states of the heart. According toan unknown 4-D motion vector field the ECG-gated FBP imagesare deformed and accumulated to a sum volume for representingthe status of a particular heart phase. In an iterative optimizationprocedure the 4-D motion vector field is computed by registeringthe sum volume to a reference volume of the same heart phase.The negative normalized cross correlation (NCC) of both volumesis used as a cost function. In this paper, the reference image isgenerated using the prior image constrained compressed sensing(PICCS) algorithm combined with the improved total variation(iTV). First preliminary experiments on clinical porcine datasets show promising results. CMHPR reduces streak artifactsand simultaneously preserves sharp edges without producing theartificial comic-like appearance of the PICCS + iTV referencevolume.

4D photogeometric face recognition with time-of-flight sensors - Bauer, Sebastian; Wasza, Jakob; Müller, Kerstin; Hornegger, Joachim (2011 IEEE Workshop on Applications of Computer Vision, WACV 2011, 2011)
Methods for 2D/3D face recognition typically combine results obtained independently from the 2D and 3D data, respectively. There has not been much emphasis on data fusion at an early stage, even though it is at least potentially more powerful to exploit possible synergies between the two modalities. In this paper, we propose photogeometric features that interpret both the photometric texture and geometric shape information of 2D manifolds in a consistent manner. The 4D features encode the spatial distribution of gradients that are derived generically for any scalar field on arbitrary organized surface meshes. We apply the descriptor for biometric face recognition with a time-of-flight sensor. The method consists of three stages: (i) facial landmark localization with a HOG/SVM sliding window framework, (ii) extraction of photogeometric feature descriptors from time-of-flight data, using the inherent grayscale intensity information of the sensor as the 2D manifold's scalar field, (iii) probe matching against the gallery. Recognition based on the photogeometric features achieved 97.5% rank-1 identification rate on a comprehensive time-of-flight dataset (26 subjects, 364 facial images). © 2010 IEEE.

50 Shades of Gray: Effect of the Color Scale for the Assessment of Speech Disorders - Pérez Toro, Paula Andrea; Klumpp, Philipp; Vásquez-Correa, J. C.; Schuster, Maria; Nöth, Elmar; Orozco-Arroyave, Juan Rafael; Arias Vergara, Tomás (Springer Science and Business Media Deutschland GmbH, 2022)
Keywords: Speech Analysis; Grayscale; Parkinson’s Disease; AutoEncoder; Color Scales; Cleft Lip and Palate

50 Shades of Gray: Effect of the Color Scale for the Assessment of Speech Disorders - Pérez-Toro, Paula Andrea; Klumpp, Philipp; Vasquez-Correa, Juan Camilo; Schuster, Maria; Nöth, Elmar; Orozco-Arroyave, Juan Rafael; Arias Vergara, Tomás (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
Spectrograms provide a visual representation of the time-frequency variations of a speech signal. Furthermore, the color scales can be used as a pre-processing normalization step. In this study, we investigated the suitability of using different color scales for the reconstruction of spectrograms together with bottleneck features extracted from Convolutional AutoEncoders (CAEs). We trained several CAEs considering different parameters such as the number of channels, wideband/narrowband spectrograms, and different color scales. Additionally, we tested the suitability of the proposed CAE architecture for the prediction of the severity of Parkinson’s Disease (PD) and for the nasality level in children with Cleft Lip and Palate (CLP). The results showed that it is possible to estimate the neurological state for PD with Spearman’s correlations of up to 0.71 using the Grayscale, and the nasality level in CLP with F-scores of up to 0.58 using the raw spectrogram. Although the color scales improved performance in some cases, it is not clear which color scale is the most suitable for the selected application, as we did not find significant differences in the results for each color scale.
CRIS-Team Scopus Importer:2022-10-14
Keywords: AutoEncoder; Cleft Lip and Palate; Color Scales; Grayscale; Parkinson’s Disease; Speech Analysis

99mTc-MIP-1404 SPECT/CT for Patients With Metastatic Prostate Cancer: Interobserver and Intraobserver Variability in Treatment-Related Longitudinal Tracer Uptake Assessments of Prostate-Specific Membrane Antigen-Positive Lesions - Schmidkonz, Christian; Atzinger, Armin; Götz, Theresa; Beck, Michael; Ritt, Philipp; Prante, Olaf; Kuwert, Torsten; Bäuerle, Tobias; Cordes, Michael (Clinical Nuclear Medicine, 2020)
BACKGROUND: Tc-MIP-1404 is a SPECT-suitable prostate-specific membrane antigen (PSMA) ligand for detection of prostate cancer. In patients with metastatic prostate cancer, there are no data as yet on interobserver and intraobserver variability when assessing PSMA-positive lesions for longitudinal changes of tracer uptake. METHODS: Tc-MIP-1404 SPECT/CT scans of 22 patients with metastatic prostate cancer were analyzed, and each subject was imaged at 2 separate points in time, before and after treatment. Mean interval between scans was 10 months. Three independent observers visually assessed a total of 96 PSMA-positive metastases (bone, 69; lymph node, 22; viscera, 3) or local recurrences (n = 2) for longitudinal changes in tracer uptake on planar scintigraphy and SPECT/CT. All lesions were categorized as regressive, stable, or progressive based on visual findings and on peak SUV (SUVpeak) of quantitative SPECT/CT (progressive, >30% SUVpeak increase; regressive, <30% SUVpeak decrease; or stable, all others). RESULTS: Quantitative analysis of PSMA-positive lesions yielded significantly higher interobserver agreement (90.6%; 95% confidence interval [CI], 0.83%-0.96%) than visual assessments by either SPECT/CT (76.0%; 95% CI, 0.66%-0.84%) or planar scintigraphy (56.3%; 95% CI, 0.46%-0.66%). Intermethod comparison of aggregated results yielded significantly higher agreement between quantitative and visual SPECT/CT (85.1%; 95% CI, 0.80%-0.89%), as opposed to quantitative SPECT/CT and planar scintigraphy (53.1%; 95% CI, 0.47%-0.59%) or visual SPECT/CT and planar scintigraphy (54.9%; 95% CI, 0.49%-0.61%). In visual and quantitative analysis of 96 PSMA-positive lesions, the number of discrepancies ranged from 9 (9.4%) for quantitative SPECT/CT to 42 (43.8%) for planar scintigraphy. Overall reader confidence was higher for SPECT/CT than for planar scintigraphy (P < 0.001). Intraobserver agreement was near-perfect for all methods, whether SPECT/CT (visual, all κ = 0.94-0.97; quantitative κ = 0.94-0.98) or planar scintigraphy (all κ = 0.90-0.94). CONCLUSIONS: Quantitative evaluation of longitudinal change in tracer uptake by PSMA-positive lesions measured via SPECT/CT is superior to visual interpretation of images by planar scintigraphy or SPECT/CT. Compared with visual evaluation, quantitative SPECT/CT is highly reproducible, showing near-perfect agreement among observers and higher reader confidence.
CRIS-Team Scopus Importer:2020-01-14

99m Tc-MIP-1404-SPECT/CT for the detection of PSMA-positive lesions in 225 patients with biochemical recurrence of prostate cancer - Schmidkonz, Christian; Hollweg, Claudia; Beck, Michael; Reinfelder, Julia; Götz, Theresa; Sanders, James Chester; Schmidt, Daniela; Prante, Olaf; Bäuerle, Tobias; Uder, Michael; Wullich, Bernd; Goebell, Peter; et al. (Prostate, 2018)
99m Tc-MIP-1404 (Progenics Pharmaceuticals, Inc., New York, NY) is a novel, SPECT-compatible 99m Tc-labeled PSMA inhibitor for the detection of prostate cancer. We present results of its clinical use in a cohort of 225 men with histologically confirmed prostate cancer referred for workup of biochemical relapse.From April 2013 to April 2017, 99m Tc-MIP1404-scintigraphy was performed in 225 patients for workup of PSA biochemical relapse of prostate cancer. Whole-body planar and SPECT/CT images of the lower abdomen and thorax were obtained 3-4 h p.i. of 710 ± 64 MBq 99m Tc-MIP-1404. Images were visually analyzed for presence and location of abnormal uptake. In addition, quantitative analysis of the SPECT/CT data was carried out on a subset of 125 patients. Follow-up reports of subsequent therapeutic interventions were available for 59% (139) of all patients.Tracer-positive lesions were detected in 77% (174/225) of all patients. Detections occurred at the area of local recurrence in the prostate in 25% of patients (or a total of 56), with metastases in lymph nodes in 47% (105), bone in 27% (60), lung in 5% (12), and other locations in 2% (4) of patients. Detection rates were 90% at PSA levels >=2 ng/mL and 54% below that threshold. Lesional SUVmax values were, on average, 32.2 ± 29.6 (0.8-142.2), and tumor-to-normal ratios 146.6 ± 160.5 (1.9-1482.4). The PSA level correlated significantly with total uptake of MIP-1404 in tumors (P < 0.001). Furthermore, total tumor uptake was significantly higher in patients with Gleason scores >=8 compared to those with Gleason scores <=7 (P < 0.05). In patients with androgen deprivation therapy, the detection rate was significantly higher compared to patients without androgen deprivation therapy (86% vs 71%, P < 0.001). Based on 99m Tc-MIP-1404-imaging and other information, an interdisciplinary tumor board review recommended changes to treatment plans in 74% (104/139) of those patients for whom the necessary documentation was available.SPECT/CT with 99m Tc-labeled MIP-1404 has a high probability in detecting PSMA-positive lesions in patients with elevated PSA. Statistical analysis disclosed significant relationship between quantitative 99m Tc-MIP-1404 uptake, PSA level, and Gleason score.
EVALuna2:13874

A 2D dilated residual U-net for multi-organ segmentation in thoracic CT - Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas (CEUR Workshop Proceedings, 2019)
Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer. Accurate segmentation of organs surrounding tumours helps account for the variation in position and morphology inherent across patients, thereby facilitating adaptive and computer-assisted radiotherapy. Although manual delineation of OARs is still highly prevalent, it is prone to errors due to complex variations in the shape and position of organs across patients, and low soft tissue contrast between neigh-bouring organs in CT images. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. In this paper, we propose a deep learning framework to segment OARs in thoracic CT images, specifically for the: heart, esophagus, trachea and aorta. Our approach employs dilated convolutions and aggregated residual connections in the bottleneck of a U-Net styled network, which incorporates global context and dense information. Our method achieved an overall Dice score of 91.57% on 20 unseen test samples from the ISBI 2019 SegTHOR challenge.
CRIS-Team Scopus Importer:2019-05-03

A 2D driven 3D vessel segmentation algorithm for 3D digital subtraction angiography data - Spiegel, Martin; Redel, T.; Struffert, Tobias; Hornegger, Joachim; Dörfler, Arnd (Physics in Medicine and Biology, 2011)
Cerebrovascular disease is among the leading causes of death in western industrial nations. 3D rotational angiography delivers indispensable information on vessel morphology and pathology. Physicians make use of this to analyze vessel geometry in detail, i.e. vessel diameters, location and size of aneurysms, to come up with a clinical decision. 3D segmentation is a crucial step in this pipeline. Although a lot of different methods are available nowadays, all of them lack a method to validate the results for the individual patient. Therefore, we propose a novel 2D digital subtraction angiography (DSA)-driven 3D vessel segmentation and validation framework. 2D DSA projections are clinically considered as gold standard when it comes to measurements of vessel diameter or the neck size of aneurysms. An ellipsoid vessel model is applied to deliver the initial 3D segmentation. To assess the accuracy of the 3D vessel segmentation, its forward projections are iteratively overlaid with the corresponding 2D DSA projections. Local vessel discrepancies are modeled by a global 2D/3D optimization function to adjust the 3D vessel segmentation toward the 2D vessel contours. Our framework has been evaluated on phantom data as well as on ten patient datasets. Three 2D DSA projections from varying viewing angles have been used for each dataset. The novel 2D driven 3D vessel segmentation approach shows superior results against state-of-the-art segmentations like region growing, i.e. an improvement of 7.2% points in precision and 5.8% points for the Dice coefficient. This method opens up future clinical applications requiring the greatest vessel accuracy, e.g. computational fluid dynamic modeling. © 2011 Institute of Physics and Engineering in Medicine.

A 3-D Projection Model for X-ray Dark-field Imaging - Felsner, Lina; Hu, Shiyang; Maier, Andreas; Bopp, Johannes; Ludwig, Veronika; Anton, Gisela; Riess, Christian (Scientific Reports, 2019)

A 3-D Projection Model for X-ray Dark-field Imaging - Hu, Shiyang; Felsner, Lina; Maier, Andreas; Ludwig, Veronika; Anton, Gisela; Riess, Christian (2018)

A 3-D Scattering Model for Orientation-dependent X-ray Dark-field Imaging - Hu, Shiyang; Maier, Andreas; Hornegger, Joachim; Bayer, Florian; Weber, Thomas; Anton, Gisela; Riess, Christian (Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)

A Bayesian approach to learn and classify 3-D objects from intensity images - Hornegger, Joachim; Niemann, Heinrich (Proceedings of the 12th International Conference on Pattern Recognition (ICPR), 1994)

A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery - Buerhop-Lutz, Claudia; Deitsch, Sergiu; Maier, Andreas; Gallwitz, Florian; Berger, Stephan; Doll, Bernd; Hauch, Jens; Camus, Christian; Brabec, Christoph (35th European Photovoltaic Solar Energy Conference and Exhibition, 2018)
In this paper a dataset consisting of 2,426 solar cells extracted from high-resolution electroluminescence (EL) images is used for automated defect probability recognition. The collected images contain both functional and defective solar cells with varying degrees of degradation both in monocrystalline and polycrystalline solar modules. The images were labeled by expert who categorized the solar cells by the likelihood of a defect within each image. The labeled images can be used for development of computer vision and machine learning methods for automatic detection of different defects, like cracks, fracture interconnects, PID, and cell quality and for the purpose of predicting the power efficiency los
Keywords: EL Imaging; Machine Learning; Visual Inspection

Abnormality Detection for Capsule Endoscope Images Based on Support Vector Machines - Timotius, Ivanna; Miaou, Shaou-Gang; Valdeavilla, Edreen Bryan; Liu, Yi-Hung (Biomedical Engineering-Applications Basis Communications, 2012)
Wireless capsule endoscopy (WCE) represents a significant technical breakthrough for the investigation of intestines. It can be used to examine entire section of the intestines, including the blind section that is not reachable with a traditional endoscope. However, one problem with this new technology is that too many images need to be examined by eyes to detect the normal and/or abnormal images and it becomes a burden to physicians. This paper presents some potential methods for an automatic detection system to identify suspected capsule endoscope images containing either chyme blocked, suspected blood indicator, or white spot abnormality in order to reduce this burden. These methods use color and texture of images as recognition features for the classifiers such as support vector machines (SVM), imbalanced SVM, and total margin-based adaptive fuzzy SVM. For comparison, the nearest neighbor (NN) classifier is also considered. Experimental results, carried out on 10-runs of 5-fold cross validation, show that the combination of hue-saturation (HS) histogram using relevant features (64 bins), image downsampling factor by 1, and TAF-SVM performs the best. The resulting accuracy obtained is 98.13% and the computational time during the testing phase is below 0.5 seconds per image, which is useful for practical applications.
Keywords: Color histogram; Texture; Chyme blocked; Suspected blood indicator; White spot abnormality

A Boosting Approach for Multiple Sclerosis Lesion Segmentation in Multi-Spectral 3D MRI - Wels, Michael; Huber, Martin; Hornegger, Joachim (3rd Russian-Bavarian Conference on Biomedical Engineering, 2007)

Abschlussbericht Software für ein Risiko-Management-System - Wilke, Peter; et al. (2002)
UnivIS-Import:2016-06-30:Pub.2002.tech.IMMD.IMMD2._absch
Keywords: Risiko-management-system

Absolute quantification in SPECT - Ritt, Philipp; Vija, Hans; Hornegger, Joachim; Kuwert, Torsten (European Journal of Nuclear Medicine and Molecular Imaging, 2011)
Single-photon emission computed tomography (SPECT) allows the three-dimensional visualization of radioactivity within the human body and is widely used for clinical purposes. In SPECT, image quality is compromised by several factors including photon attenuation, photon scatter, the partial volume effect, and motion artefacts. These variables also confound the capacity of SPECT to quantify the concentration of radioactivity within given volumes of interest in absolute units, e.g. as kilobecquerels per cubic centimetre. In the last decade, considerable technical progress has been achieved in SPECT image reconstruction, involving, in particular, the development of iterative image reconstruction techniques. Furthermore, hybrid cameras integrating a SPECT camera with an X-ray CT scanner have become commercially available. These systems allow the acquisition of SPECT and CT datasets registered to each other with a high anatomical accuracy. First studies have shown that iterative SPECT image reconstruction techniques incorporating information from SPECT/CT image datasets greatly increase the accuracy of SPECT in quantifying radioactivity concentrations in phantoms and also in humans. This new potential of SPECT may improve not only diagnostic accuracy, but also dosimetry for internal radiotherapy. © 2011 Springer-Verlag.

Absolute SPECT/CT quantification of cerebral uptake of 99mTc-HMPAO for patients with neurocognitive disorders - Welz, Friedrich; Sanders, James Chester; Kuwert, Torsten; Maler, Juan; Kornhuber, Johannes; Ritt, Philipp (Nuklearmedizin-Nuclear Medicine, 2016)
It was reported from planar imaging studies that the cerebral uptake of injected 99mTc-HMPAO activity is about 4-7% in humans. Recent work has shown that modern SPECT/CT devices are able to quantify the tissue concentration of radioactivity in vivo in absolute units (Bq/ml), while avoiding the limitations of planar techniques. The aims of this study were (a) to determine the cerebral uptake of 99mTc-HMPAO in absolute units in SPECT/CT, (b) to investigate potential differences in absolute tracer uptake for patients suspected of dementia.We performed 99mTc-HMPAO SPECT/CT in 65 patients with suspected dementia. 99mTc-HMPAO uptake was determined using a previously published quantitative SPECT/CT protocol. The absolute HMPAO uptake and the results of a regionalized analysis were compared for MMSE and NINCDS-ADRDA based patient groups.The mean absolute uptake of 99mTc-HMPAO for our patient population was 4.3 ± 0.8% of the injected dose. The uptake, as well as the regionalized analysis yielded significantly different results for low (<=23) and high (>23) MMSE groups and also for some of the NINCDS-ADRDA groups.Our results show that the absolute cerebral uptake of 99mTc-HMPAO is in the range of previously reported results, obtained by planar techniques. Absolute uptake is significantly different between the patient groups.
EVALuna2:13833

Abstract: Are Fast Labeling Methods Reliable?: A Case Study of Computer-aided Expert Annotations on Microscopy Slides - Marzahl, Christian; Bertram, Christof A.; Aubreville, Marc; Petrick, Anne; Weiler, Kristina; Gläsel, Agnes C.; Fragoso, Marco; Merz, Sophie; Bartenschlager, Florian; Hoppe, Judith; Langenhagen, Alina; Jasensky, Anne Katherine; et al. (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Abstract: Automatic CAD-RADS Scoring using Deep Learning - Denzinger, Felix; Wels, Michael; Breininger, Katharina; Gülsün, Mehmet A.; Schöbinger, Max; André, Florian; Buß, Sebastian; Görich, Johannes; Sühling, Michael; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

Abstract: 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 (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

Abstract: Automatic Malignancy Estimation for Pulmonary Nodules from CT Images - Mentl, Katrin; Saffoury, Rimon; Maier, Andreas (Informatik Aktuell, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.abstra_7

Abstract: Coronary Artery Plaque Characterization from CCTA Scans Using DL and Radiomics - Denzinger, Felix; Wels, Michael; Breininger, Katharina; Reidelshöfer, Anika; Eckert, Joachim; Sühling, Michael; Schmermund, Axel; Maier, Andreas (BVM Workshop 2020, 2020)
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

Abstract: Deep Learning-based Quantification of Pulmonary Hemosiderophages in Cytology Slides - Marzahl, Christian; Aubreville, Marc; Bertram, Christof A.; Stayt, Jason; Jasensky, Anne Katherine; Bartenschlager, Florian; Fragoso, Marco; Barton, Ann K.; Elsemann, Svenja; Jabari, Samir; Krauth, Jens; Madhu, Prathmesh; et al. (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Abstract: Fiducial Marker Recovery and Detection From Severely Truncated Data in Navigation-Assisted Spine Surgery - Fan, Fuxin; Kreher, Björn W.; Keil, Holger; Maier, Andreas; Huang, Yixing (Bildverarbeitung für die Medizin 2023, 2023)
Fiducial markers are commonly used in navigation-assisted minimally invasive spine surgery and they help transfer image coordinates into real-world coordinates. In practice, these markers might be located outside the field-of-view (FOV) of C-arm cone beam computed tomography (CBCT) systems. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation. In this work, we propose two fiducial marker detection methods: direct detection from distorted markers (direct method) and detection after marker recovery (recovery method) [1]. For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed. For marker recovery, a task-specific data preparation strategy is proposed to recover markers from severely truncated data. Afterwards, a conventional marker detection algorithm is applied for position detection. The networks in both methods are trained only on simulated data and the two methods are evaluated on simulated data and real cadaver data. The direct method achieves 100% detection rates within 1 mm detection error on simulated data with normal truncation and simulated data with heavier noise, but only detect 94.6% markers in extremely severe truncation case. The recovery method detects all the markers successfully in three test data sets and around 95% markers are detected within 0.5 mm error. For real cadaver data, both methods achieve 100% marker detection rates with mean registration error below 0.2 mm. Our experiments demonstrate that the direct method is capable of detecting distorted markers accurately and the recovery method with the task-specific data preparation strategy has high robustness and generalizability on various data sets. The task-specific data preparation is able to reconstruct structures of interest outside the FOV from severely truncated data better than conventional data preparatio
Keywords: Data truncation; Deep learning; CBCT; Spine surgery; Fiducial marker

Abstract: Pan-tumor CAnine CuTaneous Cancer Histology (CATCH) Dataset - Wilm, Frauke; Fragoso, Marco; Marzahl, Christian; Qiu, Jingna; Puget, Chloé; Diehl, Laura; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas; Breininger, Katharina; Aubreville, Marc (Informatik aktuell, 2023)
The identification of tumor regions on cutaneous tissue sections and the subsequent differentiation into individual tumor subtypes are routine tasks for veterinary pathologists. However, manual tumor delineation can be time-consuming and morphological similarities of tumor types can make the subtyping task difficult. Deep learning-based algorithms have been successfully applied for both tasks but require a large amount of annotated data to learn robust representations. Acquiring the annotations necessary to train these algorithms can be time-consuming and requires expert knowledge. Publicly available datasets reduce this annotation overhead and allow for a comparison of image analysis algorithms trained for the task at hand. We published a publicly available dataset of 350 whole slide images (WSIs) of seven different canine cutaneous tumors. These were annotated for the tumor subtypes and additionally six skin tissue classes. Overall, the database includes 12,424 polygon annotations for 13 classes, which exceeds most publicly available datasets in annotation extent and label diversity. In our dataset report, we validated the provided annotations through inter-rater experiments on a subset of the presented dataset, where three pathologists demonstrated high consistency in labeling tissue structures, especially for tumor annotations. Furthermore, we performed a technical validation by first training a deep neural network for tissue phenotyping. The model segmented the WSI into tumor and five skin tissue classes, where we achieved a class-averaged Jaccard coefficient of 0.7047 and a Jaccard coefficient of 0.9044 for tumor in particular. Afterward, we classified the segmented tumor regions into the tumor subtypes and achieved a slide-level accuracy of 0.9857. These baseline results provide a starting point for the development of more advanced algorithms. Previous works have shown that canine and human cutaneous tumors share various histologic patterns, which extends the added value of the presented dataset beyond veterinary pathology [1].
Created from Fastlane, Scopus look-up

Abstract: QuaSI - Quantile Sparse Image A Prior for Spatio-Temporal Denoising of Retinal OCT Data - Schirrmacher, Franziska; Köhler, Thomas; Husvogt, Lennart; Fujimoto, James G.; Hornegger, Joachim; Maier, Andreas (Informatik aktuell, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.abstra

Abstract: Robust Multi-Scale Anatomical Landmark Detection in Incomplete 3D-CT Data - Ghesu, Florin-Cristian; Georgescu, Bogdan; Grbic, Sasa; Maier, Andreas; Hornegger, Joachim; Comaniciu, Dorin (Informatik Aktuell, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.abstra_0

Abstract: Simultaneous Estimation of X-ray Back-scatter and Forward-scatter using Multi-task Learning - Roser, Philipp; Zhong, Xia; Birkhold, Annette; Preuhs, Alexander; Syben-Leisner, Christopher; Hoppe, Elisabeth; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Abstract: VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data - Thamm, Florian; Jürgens, Markus; Ditt, Hendrik; Maier, Andreas (BVM Workshop, 2021)
Computed Tomography Angiography (CTA) is one of the most commonly used modalities in the diagnosis of cerebrovascular diseases like ischemic strokes. Usually, the anatomy of interest in ischemic stroke cases is the Circle of Willis and its peripherals, the cerebral arteries, as these vessels are the most prominent candidates for occlusions. The diagnosis of occlusions in these vessels remains challenging, not only because of the large amount of surrounding vessels but also due to the large number of anatomical variants. We propose a fully automated image processing and visualization pipeline, which provides a full segmentation and modelling of the cerebral arterial tree for CTA data. The model itself enables the interactive masking of unimportant vessel structures e.g. veins like the Sinus Sagittalis, and the interactive planning of shortest paths meant to be used to prepare further treatments like a mechanical thrombectomy. Additionally, the algorithm automatically labels the cerebral arteries (Middle Cerebral Artery left and right, Anterior Cerebral Artery short, Posterior Cerebral Artery left and right) detects occlusions or interruptions in these vessels. The proposed pipeline does not require a prior non-contrast CT scan and achieves a comparable segmentation appearance as in a Digital Subtraction Angiography (DSA).

Accelerated C-arm Reconstruction by Out-of-Projection Prediction - Hofmann, Hannes; Keck, Benjamin; Hornegger, Joachim (Bildverarbeitung für die Medizin 2010, 2010)

Accelerated Non-contrast-enhanced Morphological and Functional Magnetic Resonance Angiography - Hutter, Jana (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014)
Cardiovascular diseases such as stroke, stenosis, peripheral or renal artery disease require accurate angiographic visualization techniques both for diagnosis and treatment planning. Beside the morphological imaging, the in-vivo acquisition of blood flow information gained increasing clinical importance in recent years. Non-contrast-enhanced Magnetic Resonance Angiography (nceMRA) provides techniques for both fields. For morphological imaging, Time of Flight (TOF) and magnetization-prepared balanced Steady State Free Precession (mp-bSSFP) offer non-invasive, ionizing-radiation free and user independent alternatives to clinically established methods such as Digital Subtraction Angiography, Computed Tomography or Ultrasound. In the field of functional imaging, unique novel possibilities are given with three-directional velocity fields, acquired simultaneously to the morphological information using Phase Contrast Imaging (PCI). But the wider clinical use of nceMRA is still hampered by long acquisition times. Thus, accelerating nceMRA is a problem of high relevance and with great potential clinical impact. In this thesis, acceleration strategies based on k-sampling below the Nyquist limit and adapted reconstruction techniques, combining parallel MRI (pMRI) methods with Compressed Sensing (CS), are developed for both types of nceMRA methods. This includes contributions to all relevant parts of the reconstruction algorithms, the sampling strategy, the regularization technique and the optimization method. For morphological imaging, a novel analytical pattern combining aspects of pMRI and CS, called the MICCS pattern, is proposed in combination with an adapted Split Bregman algorithm. This allows for a reduction in the acquisition time for peripheral TOF imaging of the entire lower vasculature from over 30 minutes to less than 8 minutes. Further acceleration is achieved for 3-D free-breathing renal angiography using mp-bSSFP, where the entire volume can be acquired in less than 1 minute instead of over 8 minutes. In addition, organ based evaluations including the vessel sharpness at important positions show the diagnostic usability and the increased accuracy over clinically established acceleration methods. For PCI, advances are achieved with a dedicated novel sampling strategy, called I-VT sampling, including interleaved variations for all dimensions. Furthermore, two novel regularization techniques for PCI are developed in this thesis. First, a novel temporally masked and weighted strategy focusing on enhanced temporal fidelity, referred to as TMW is developed. This fully automatic approach uses dynamic and static vessel masks to guide the influence specifically to the static areas. Second, the low rank and sparse decomposition model, is extended to PCI, combined with adapted sparsity assumptions and the unconstrained Split Bregman algorithm. These methods are successfully applied to the carotid bifurcation, a region with a huge demand of significant acceleration as well high spatial and temporal accuracy of the flow values. But all algorithmic contributions exploit inherent properties of the acquisition technique, and thus can be applied for further applications. In summary, the main contribution of this thesis is significant acceleration of nceMRA achieved with novel sampling, regularization and optimization elements.<br
UnivIS-Import:2016-07-26:Pub.2014.tech.IMMD.IMMD5.accele
Keywords: Bildverarbeitung; Bildrekonstruktion; Magnetresonanzangiographie

Accelerated Whole-Heart Coronary Imaging using Multiple Breath-holds and Compressed Sensing Monitored by Self-navigation - Forman, Christoph; Piccini, Davide; Hutter, Jana; Grimm, Robert; Hornegger, Joachim; Zenge, Michael O. (Proceedings of the 20th Annual Meeting of ISMRM, 2012)

Accelerating Multi-Echo Water-Fat MRI with a Joint Locally Low-Rank and Spatial Sparsity-Promoting Reconstruction - Lugauer, Felix; Nickel, Dominik; Wetzl, Jens; Kiefer, Berthold; Hornegger, Joachim; Maier, Andreas (Magnetic Resonance Materials in Physics Biology and Medicine, 2016)
UnivIS-Import:2017-03-24:Pub.2016.tech.IMMD.IMMD5.accele
Keywords: Multi-echo Dixon; Quantitative water-fat MRI; Compressed sensing; Locally low-rank

Accuracy of Fatty Acid Quantification using Bipolar Multi-Echo MRI for Varying Numbers of Echoes - Schneider, Manuel; Lugauer, Felix; Hoppe, Elisabeth; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.accura_9

Accuracy of x-ray image-based 3D localization from two C- arm views: A comparison between an ideal system and a real device - Brost, Alexander; Strobel, Norbert; Yatziv, Liron; Gilson, Wesley; Meyer, Bernhard; Hornegger, Joachim; Lewin, Jonathan; Wacker, Frank (Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2009)
C-arm X-ray imaging devices are commonly used for minimally invasive cardiovascular or other interventional procedures. Calibrated state-of-the-art systems can, however, not only be used for 2D imaging but also for three-dimensional reconstruction either using tomographic techniques or even stereotactic approaches. To evaluate the accuracy of X-ray object localization from two views, a simulation study assuming an ideal imaging geometry was carried out first. This was backed up with a phantom experiment involving a real C-arm angiography system. Both studies were based on a phantom comprising five point objects. These point objects were projected onto a flat-panel detector under different C-arm view positions. The resulting 2D positions were perturbed by adding Gaussian noise to simulate 2D point localization errors. In the next step, 3D point positions were triangulated from two views. A 3D error was computed by taking differences between the reconstructed 3D positions using the perturbed 2D positions and the initial 3D positions of the five points. This experiment was repeated for various C-arm angulations involving angular differences ranging from 15o to 165o. The smallest 3D reconstruction error was achieved, as expected, by views that were 90o degrees apart. In this case, the simulation study yielded a 3D error of 0.82 mm ± 0.24 mm (mean ± standard deviation) for 2D noise with a standard deviation of 1.232 mm (4 detector pixels).The experimental result for this view configuration obtained on an AXIOM Artis C-arm (Siemens AG, Healthcare Sector, Forchheim, Germany) system was 0.98 mm ± 0.29 mm, respectively. These results show that state-of-the-art C-arm systems can localize instruments with millimeter accuracy, and that they can accomplish this almost as well as an idealized theoretical counterpart. High stereotactic localization accuracy, good patient access, and CT-like 3D imaging capabilities render state-of-the-art C-arm systems ideal devices for X-ray based minimally invasive procedures. © 2009 SPIE.

Accurate fatty acid composition estimation of adipose tissue in the abdomen based on bipolar multi-echo MRI - Schneider, Manuel; Janas, Gemini; Lugauer, Felix; Hoppe, Elisabeth; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Magnetic Resonance in Medicine, 2019)
Purpose: To develop a bipolar multi-echo MRI method for the accurate estimation of the adipose tissue fatty acid composition (FAC) using clinically relevant protocols at clinical field strength.
CRIS-Team WoS Importer:2019-04-09

Accurate image reconstruction using real C-arm data from a Circle-plus-arc trajectory - Hoppe, Stefan; Hornegger, Joachim; Dennerlein, Frank; Lauritsch, Günter; Noo, Frederic (International Journal of Computer Assisted Radiology and Surgery, 2012)
Objective Developing an efficient tool for accurate threedimensional imaging from projections measured with C-arm systems. Material and methods A circle-plus-arc trajectory, which is complete and thus amenable to accurate reconstruction, is used. This trajectory is particularly attractive as its implementation does not require moving the patient. For reconstruction, we use the "M-line method", which allows processing the data in the efficient filtered backprojection mode. This method also offers the advantage of not requiring an ideal data acquisition geometry, i.e., the M-line algorithm can account for known deviations in the scanning geometry, which is important given that sizeable deviations are generally encountered in C-arm imaging. Results A robust implementation scheme of the "M-line method" that applies straightforwardly to real C-arm data is presented. In particular, a numerically stable technique to compute the view-dependent derivative with respect to the source trajectory parameter is applied, and an efficient way to compute the p-line backprojection intervals via a polygonal weighting mask is presented. Projection data of an anthropomorphic thorax phantom were acquired on a medical C-arm scanner and used to demonstrate the benefit of using a complete data acquisition geometry with an accurate reconstruction algorithm versus using a state-of-theart implementation of the conventional Feldkamp algorithm with a circular short scan of cone-beam data. A significant image quality improvement based on visual assessment is shown in terms of cone-beam artifacts. © CARS 2011.

Accurate laser scanner to camera calibration with application to range sensor evaluation - Fürsattel, Peter; Plank, Claus; Maier, Andreas; Riess, Christian (IPSJ Transactions on Computer Vision and Applications, 2017)

Accurate Measurements with Off-the-Shelf Range Cameras - Fürsattel, Peter (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2018)
Distance cameras have gained large popularity in the last years. With more than 24 million Microsoft Kinect units sold and the proliferation of 3-D sensors for biometric authentication, these cameras have reached the mass market. Distance cameras capture an image in which each pixel encodes the distance to its corresponding point in the scene. This opens up new application possibilities which are difficult or even impossible to implement with normal gray-level or color cameras. These new applications are particular useful if they can be implemented with low-cost consumer 3-D cameras. However, this is problematic as these sensors have only limited accuracy compared to professional measurement systems and are thus not yet sufficient for many applications. In this thesis, several aspects that affect the accuracy of time-of-flight and structured light cameras are discussed. The calibration of cameras, i. e. the calculation of an exact camera model, is of major importance. The estimation of these models requires point correspondences between the scene and the camera image. Whenever high accuracy is required, it is recommended to use calibration patterns such as checkerboards. This thesis introduces two methods, which find checkerboards more reliably and accurately than existing algorithms. The evaluation of the measurement errors of distance cameras requires reference values that are considerably more accurate than those of the camera. This thesis presents a method that allows using a terrestrial laser scanner to acquire such data. However, before the reference data can be used for error analysis, it is necessary to transform the measurements of both sensors into a common coordinate system. For this purpose, an automatic method was developed that reliably calculates the unknown transformation based on a single calibration scene. The accuracy of this approach is confirmed in several experiments and clearly exceeds the accuracy of the competing state-of-the-art method. In addition, it is possible to generate reference distance images with this method, which can subsequently be used for the evaluation of distance cameras. Time-of-flight (ToF) cameras have some error sources that are characteristic for this measurement principle. In order to be able to better compensate for these errors, it is first necessary to investigate the nature of these errors. This thesis also presents a comprehensive, standardized evaluation of the systematic errors of ToF cameras. For this purpose, six experiments are defined, which are subsequently carried out with eight ToF cameras. The evaluation of these experiments shows that the characteristic errors are differently pronounced with the investigated cameras, but nonetheless, can be observed even with the most recent models. Finally, a new calibration method for structured-light sensors is proposed, as well as an algorithm which refines parametric models of objects. The calibration method allows calculating a complete sensor model based on two or more images, even if the projected pattern is unknown. This is particularly necessary if the sensor does not use a regular projector but emits the pattern with a diffractive optical element. This part of the thesis also presents a novel refinement method for parametric models that uses the developed camera model. The evaluation results show that the proposed method computes more accurate model parameters than state-of-the-art fitting algorithms.<br
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.accura
Keywords: Range Camera; Structured Light

Accurate Regression-Based 4D Mitral Valve Surface Reconstruction from 2D+t MRI Slices - Vitanovski, Dime; Tsymbal, Alexey; Ionasec, Razvan; Schmidt, Michaela; Greiser, Andreas; Müller, Edgar; Lu, Xiaoguang; Funka-Lea, Gareth; Hornegger, Joachim; Comaniciu, Dorin (Springer-verlag, 2011)
Cardiac MR (CMR) imaging is increasingly accepted as the gold standard for the evaluation of cardiac anatomy, function and mass. The multi-plan ability of CMR makes it a well suited modality for evaluation of the complex anatomy of the mitral valve (MV). However, the 2D slice-based acquisition paradigm of CMR limits the 4D capabilities for precise and accurate morphological and pathological analysis due to long through-put times and protracted study. In this paper we propose a new CMR protocol for acquiring MR images for 4D MV analysis. The proposed protocol is optimized regarding the number and spatial configuration of the 2D CMR slices. Furthermore, we present a learning- based framework for patient-specific 4D MV segmentation from 2D CMR slices (sparse data). The key idea with our Regression-based Surface Reconstruction (RSR) algorithm is the use of available MV models from other imaging modalities (CT, US) to train a dynamic regression model which will then be able to infer the absent information pertinent to CMR. Extensive experiments on 200 transesophageal echochardiographic (TEE) US and 20 cardiac CT sequences are performed to train the regression model and to define the CMR acquisition protocol. With the proposed acquisition protocol, a stack of 6 parallel long-axis (LA) planes, we acquired CMR patient images and regressed 4D patient-specific MV model with an accuracy of 1.5±0.2 mm and average speed of 10 sec per volume. © 2011 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD5.accura

A Common Framework for Ambient Illumination in the Dichromatic Reflectance Model - Riess, Christian; Jordan, Johannes Michael; Angelopoulou, Elli (IEEE 12th International Conference on Computer Vision Workshops, 2009)

A Comparative Error Analysis of Current Time-of-Flight Sensors - Fürsattel, Peter; Placht, Simon; Balda, Michael; Schaller, Christian; Hofmann, Hannes; Maier, Andreas; Riess, Christian (IEEE Transactions on Computational Imaging, 2016)

A comparison of methods for adapting 177Lu dose-voxel-kernels to tissue inhomogeneities - Götz, Theresa; Schmidkonz, Christian; Lang, E. W.; Maier, Andreas; Kuwert, Torsten; Ritt, Philipp (Physics in Medicine and Biology, 2019)
In 177Lu radionuclide therapies, dosimetry is used for determining patient-individual dose burden. Standard approaches provide whole organ doses only. For assessing dose heterogeneity inside organs, voxel-wise dosimetry based on 3D SPECT/CT imaging could be applied. Often, this is achieved by convolving voxel-wise time-activity-curves with appropriate dose-voxel-kernels (DVK). The DVKs are meant to model dose deposition, and can be more accurate if modelled for the specific tissue type under consideration. In literature, DVKs are often not adapted to these inhomogeneities, or simple approximation schemes are applied. For 26 patients, which had previously undergone a 177Lu-PSMA or -DOTATOC therapy, decay maps, mass-density maps as well as tissue-type maps were derived from SPECT/CT acquisitions. These were used for a voxel-based dosimetry based on convolution with DVKs (each of size (4.8mm)3) obtained by four different DVK methods proposed in literature. The simplest only considers a spatially constant soft-tissue DVK (herein named 'constant'), while others either take into account only the local density of the center voxel of the DVK (herein named 'center-voxel') or scale each voxel linearly according to the proper mass density deduced from the CT image (herein named 'density') or considered both the local mass density as well as the direct path between the center voxel and any voxel in its surrounding (herein named 'percentage'). Deviations between resulting dose values and those from full Monte-Carlo simulations (MC simulations) were compared for selected organs and tissue-types. For each DVK method, inter-patient variability was considerable showing both under- and over-estimation of energy dose compared to the MC result for all tissue densities higher than soft tissue. In kidneys and spleen, 'constant' and 'density'-scaled DVKs achieved estimated doses with smallest deviations to the full MC gold standard (∼5%-8% underestimation). For low and high density tissue types such as lung and adipose or bone tissue, alternative DVK methods like 'center-voxel'- and 'percentage'- scaled achieved superior results, respectively. Concerning computational load, dose estimation with the DVK method 'constant' needs about 1.1 s per patient, center-voxel scaling amounts to 1.2 s, density scaling needs 1.4 s while percentage scaling consumes 860.3 s per patient. In this study encompassing a large patient cohort and four different DVK estimation methods, no single DVK-adaption method was consistently better than any other in case of soft tissue kernels. Hence in such cases the simplest DVK method, labeled 'constant', suffices. In case of tumors, often located in tissues of low (lung) or high (bone) density, more sophisticated DVK methods excel. The high inter-patient variability indicates that for evaluating new algorithms, a sufficiently large patient cohort needs to be involved.
CRIS-Team Scopus Importer:2020-02-21
Keywords: dose-voxel-kernel; dosimetry; SPECT/CT

A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research - Aubreville, Marc; Bertram, Christof A.; Donovan, Taryn A.; Marzahl, Christian; Maier, Andreas; Klopfleisch, Robert (Scientific Data, 2020)
Canine mammary carcinoma (CMC) has been used as a model to investigate the pathogenesis of human breast cancer and the same grading scheme is commonly used to assess tumor malignancy in both. One key component of this grading scheme is the density of mitotic figures (MF). Current publicly available datasets on human breast cancer only provide annotations for small subsets of whole slide images (WSIs). We present a novel dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance. A second expert blindly assigned labels, and for non-matching labels, a third expert assigned the final labels. Additionally, we used machine learning to identify previously undetected MF. Finally, we performed representation learning and two-dimensional projection to further increase the consistency of the annotations. Our dataset consists of 13,907 MF and 36,379 hard negatives. We achieved a mean F1-score of 0.791 on the test set and of up to 0.696 on a human breast cancer dataset.
CRIS-Team Scopus Importer:2020-12-04

A Cone-beam Reconstruction Algorithm for Dose-minimized Short Scan and Super Short Scan - Xia, Yan; Dennerlein, Frank; Bauer, Sebastian; Berger, Martin; Hornegger, Joachim; Maier, Andreas (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.aconeb_8
Keywords: GRK-1773

A cost constrained boosting algorithm for fast lesion detection and segmentation - Militzer, Arne; Tietjen, Christian; Hornegger, Joachim (Medical Imaging 2011: Computer-Aided Diagnosis, 2011)
Machine learning techniques like pointwise classification are widely used for object detection and segmentation. However, for large search spaces like CT images, this approach becomes computationally very demanding. Designing strong yet compact classifiers is thus of great importance for systems that ought to be clinically used as time is a limiting factor in clinical routine. The runtime of a system plays an important role in the decision about its application. In this paper we propose a novel technique for reducing the computational complexity of voxel classification systems based on the well-known AdaBoost algorithm in general and Probabilistic Boosting Trees in particular. We describe a means of incorporating a measure of hypothesis complexity into the optimization process, resulting in classifiers with lower evaluation cost. More specifically, in our approach the hypothesis generation that is performed during the AdaBoost training is no longer based only on the error of a hypothesis but also on its complexity. This leads to a reduced overall classifier complexity and thus shorter evaluation times. The validity of the approach is shown in an experimental evaluation. In a cross validation experiment, a system for automatic segmentation of liver tumors in CT images, that is based on the Probabilistic Boosting Tree, was trained with and without the proposed extension. In this preliminary study, the evaluation cost for classifying previously unseen samples could be reduced by 83% using the methods described here without losing classification accuracy. © 2011 SPIE.

Acoustic and Linguistic Analyses to Assess Early-Onset and Genetic Alzheimer's Disease - Pérez Toro, Paula Andrea; Vasquez Correa, Juan; Arias Vergara, Tomás; Klumpp, Philipp; Sierra-Castrillón, Melissa; Roldán-López, Mildred Estefania; Aguillón, David; Hincapié-Henao, Liliana; Tóbon-Quintero, Carlos Andres; Bocklet, Tobias; Schuster, Maria; Orozco-Arroyave, Juan Rafael; et al. (ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021)

Acoustic Breathiness Index for the Japanese-Speaking Population: Validation Study and Exploration of Affecting Factors - Hosokawa, Kiyohito; von Latoszek, Ben Barsties; Ferrer Riesgo, Carlos Ariel; Iwahashi, Toshihiko; Iwahashi, Mio; Iwaki, Shinobu; Kato, Chieri; Yoshida, Misao; Umatani, Masanori; Miyauchi, Akira; Matsushiro, Naoki; Inohara, Hidenori; et al. (Journal of Speech Language and Hearing Research, 2019)
Objectives The purposes of this study were to validate the Acoustic Breathiness Index (ABI) for the Japanese-speaking population and to determine whether it is independent of factors such as sex, age, and perceptual ratings of roughness. Method First, the concurrent validity of the ABI for perceptual breathiness was evaluated on the concatenations of continuous speech and sustained vowels from 288 patients with varying degrees of dysphonia. The diagnostic accuracy was examined on 343 samples with 55 additional normophonic speakers. Second, the validity related to responsiveness-to-change was estimated on 222 samples obtained before and after interventions for 111 voice-disordered patients. Third, the relationships between the ABI and other variables (i.e., perceptual hoarseness/breathiness/roughness, sex, and age) were explored using bivariate and multivariate analyses for the 288 patients. Results First, the concurrent validity and the responsiveness-to-change validity were confirmed by strong correlation coefficients of .890 and .878, respectively. Second, the receiver operating characteristic analysis showed the area under the curve to be 0.939, indicating excellent accuracy. The ABI of 3.44 exhibited a sensitivity of 76.3% and a specificity of 94.1%. Third, although bivariate analyses revealed a weak relationship between ABI and roughness and an ABI difference by age, multiple regression analyses showed a strong relation between only ABI and breathiness, without a meaningful contribution from roughness, sex, and age factors. Conclusion The study confirmed that the ABI is an accurate and specific tool to estimate breathiness levels in the Japanese-speaking population and neither roughness, sex, nor age significantly affects the ABI.
CRIS-Team Scopus Importer:2019-09-03

Acoustic characteristics of vot in plosive consonants produced by parkinson’s patients - Argüello-Vélez, Patricia; Arias-Vergara, Tomas; González-Rátiva, María Claudia; Orozco-Arroyave, Juan Rafael; Nöth, Elmar; Schuster, Maria Elke (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020)
Voice Onset Time (VOT) has been used as an acoustic measure for a better understanding of the impact of different motor speech disorders in speech production. The purpose of our paper is to present a methodology for the manual measuring of VOT in voiceless plosive sounds and to analyze its suitability to detect specific articulation problems in Parkinson’s disease (PD) patients. The experiments are performed with recordings of the diadochokinetic evaluation which consists in the rapid repetition of the syllables /pa-ta-ka/. A total of 50 PD patients and 50 healthy speakers (HC) participated in this study. Manual measurements include VOT values and also duration of the closure phase, duration of the consonant, and the maximum spectral energy during the burst phase. Results indicate that the methodology is consistent and allows the automatic classification between PD patients and healthy speakers with accuracies of up to 77 %.
CRIS-Team Scopus Importer:2020-11-06
Keywords: Acoustic analysis; Diadochokinesis; Speech processing; Voice onset time

Acoustic Modeling of Foreign Words in a German Speech Recognition System - Stemmer, Georg; Nöth, Elmar; Niemann, Heinrich (Proc. European Conf. on Speech Communication and Technology, 2001)

Acoustic Normalization of Children's Speech - Stemmer, Georg; Hacker, Christian; Steidl, Stefan; Nöth, Elmar (Proc. European Conf. on Speech Communication and Technology, 2003)

Acoustic-Prosodic Characteristics of Sleepy Speech - between Performance and Interpretation - Hönig, Florian Thomas; Batliner, Anton; Nöth, Elmar; Schnieder, Sebastian; Krajewski, Jarek (Proceedings of the 7th Biennial meeting of the Speech Prosody Special Interest Group (SProSIG) of the International Speech Communication Association (ISCA) (Speech Prosody 2014), 2014)
When we address speaker states like sleepiness, two partly competing interests can be observed: both within applications and engineering approaches, we aim at utmost performance in terms of classification or regression accuracy-which normally means using a very large feature vector and a brute force approach. The other interest is interpretation: we want to know what tells apart atypical (here: sleepy) speech from typical (here: non-sleepy) speech, i.e., their respective feature characteristics. Both interests cannot be served at the same time. In this paper, we preselect a small number of easily interpretable acoustic-prosodic features modelling spectrum and prosody, based on the literature and on the general idea of sleepiness being characterised by relaxation. Performance obtained with these single features and this small feature vector is compared with the performance obtained with a very large feature vector; moreover, we discuss to which extent the features chosen model relaxation as sleepiness characteristic.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.acoust
Keywords: Brute forcing; Interpretation; Paralinguistics; Prosody; Sleepiness

Acquisition-related motion compensation for digital subtraction angiography - Ionasec, Razvan; Heigl, Benno; Hornegger, Joachim (Computerized Medical Imaging and Graphics, 2009)
Subtraction methods in angiography are generally applied in order to enhance the visualization of blood vessels by eliminating bones and surrounding tissues from X-ray images. The main limitation of these methods is the sensitivity to patient movement, which leads to artifacts and reduces the clinical value of the subtraction images. In this paper we present a novel method for rigid motion compensation with primary application to road mapping, frequently used in image-guided interventions. Using the general concept of image-based registration, we optimize the physical position and orientation of the C-arm X-ray device, thought of as the rigid 3D transformation accounting for the patient movement. The registration is carried out using a hierarchical optimization strategy and a similarity measure based on the variance of intensity differences, which has been shown to be most suitable for fluoroscopic images. Performance evaluation demonstrated the capabilities of the proposed approach to compensate for potential intra-operative patient motion, being more resilient to the fundamental problems of pure image-based registration. © 2008 Elsevier Ltd. All rights reserved.

Active Knowledge-Based Scene Analysis - Paulus, Dietrich; Ahlrichs, Ulrike; Heigl, Benno; Denzler, Joachim; Hornegger, Joachim; Zobel, Matthias; Niemann, Heinrich (Videre - A Journal Of Computer Vision Research, 2000)

Active Knowledge Based Scene Analysis - Paulus, Dietrich; Ahlrichs, Ulrike; Heigl, Benno; Denzler, Joachim; Hornegger, Joachim; Niemann, Heinrich (Computer Vision Systems, 1999)

Acute Neuromuscular Adaptations in the Postural Control of Patients with Parkinson’s disease after Perturbed Walking - Pasluosta, Cristian Federico; Steib, Simon; Klamroth, Sarah; Gaßner, Heiko; Goßler, Julia; Hannink, Julius; Von Tscharner, Vinzenz; Pfeifer, Klaus; Winkler, Jürgen; Klucken, Jochen; Eskofier, Björn (Frontiers in Aging Neuroscience, 2017)

Adaptation in the Pronunciation Space for Non-Native Speech Recognition - Steidl, Stefan; Stemmer, Georg; Hacker, Christian; Nöth, Elmar (Interspeech 2004 ICSLP, 8th International Conference on Spoken Language Processing, Jeju Island, Korea, Proceedings, 2004)
We introduce a new technique to improve the recognition of non-native speech. The underlying assumption is that for each non-native pronunciation of a speech sound, there is at least one sound in the target language that has a similar native pronunciation. The adaptation is performed by HMM interpolation between adequate native acoustic models. The interpolation partners are determined automatically in a data-driven manner. Our experiments show that this technique is suitable for both the offline adaptation to a whole group of speakers as well as for the unsupervised online adaptation to a single speaker. Results are given both for spontaneous non-native English speech as well as for a set of read non-native German utterances.

Adaption of 3D Models to 2D X-Ray Images during Endovascular Abdominal Aneurysm Repair - Toth, Daniel; Pfister, Marcus; Maier, Andreas; Kowarschik, Markus; Hornegger, Joachim (18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I, 2015)
Endovascular aneurysm repair (EVAR) has been gaining popularity over open repair of abdominal aortic aneurysms (AAAs) in the recent years. This paper describes a distortion correction approach to be applied during the EVAR cases. In a novel workflow, models (meshes) of the aorta and its branching arteries generated from preoperatively acquired computed tomography (CT) scans are overlayed with interventionally acquired fluoroscopic images. The overlay provides an arterial roadmap for the operator, with landmarks (LMs) marking the ostia, which are critical for stent placement. As several endovascular devices, such as angiographic catheters, are inserted, the anatomy may be distorted. The distortion reduces the accuracy of the overlay. To overcome the mismatch, the aortic and the iliac meshes are adapted to a device seen in uncontrasted intraoperative fluoroscopic images using the skeletonbased as-rigid-as-possible (ARAP) method. The deformation was evaluated by comparing the distance between an ostium and the corresponding LM prior to and after the deformation. The central positions of the ostia were marked in digital subtraction angiography (DSA) images as ground truth. The mean Euclidean distance in the image plane was reduced from 19.81+/-17.14mm to 4.56+/-2.81 mm.
Keywords: computational geometry; as-rigid-as-possible; mesh deformation; abdominal aortic aneurysm; EVAR

Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems - Schirrmacher, Franziska; Köhler, Thomas; Riess, Christian (2018)

Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems - Schirrmacher, Franziska; Riess, Christian; Köhler, Thomas (IEEE Transactions on Computational Imaging, 2020)
Inverse problems play a central role for many classical computer vision and image processing tasks. Many inverse problems are ill-posed, and hence require a prior to regularize the solution space. However, many of the existing priors, like total variation, are based on ad-hoc assumptions that have difficulties to represent the actual distribution of natural images. Thus, a key challenge in research on image processing is to find better suited priors to represent natural images. In this article, we propose the Adaptive Quantile Sparse Image (AQuaSI) prior. It is based on a quantile filter, can be used as a joint filter on guidance data, and be readily plugged into a wide range of numerical optimization algorithms. We demonstrate the efficacy of the proposed prior in joint RGB/depth upsampling, on RGB/NIR image restoration, and in a comparison with related regularization by denoising approache
Keywords: Inverse problems; universal image prior; weighted quantile filter

Adaptive variational sinogram interpolation of sparsely sampled CT data - Hornegger, Joachim; Köstler, Harald; Rüde, Ulrich; Prümmer, Marcus (Proceedings of the ICPR 2006, 2006)
We present various kinds of variational PDE based methods to interpolate missing sinogram data for tomographic image reconstruction. Using the observed sinogram data we inpaint the projection data by diffusion. To overcome the problem of contour blurring we consider nonlinear and anisotropic diffusion based regularizers and include optical flow information in order to preserve the sinuodal traces corresponding to object contours in the reconstructed image. We compare our results to a spectral deconvolution based interpolation and show that the method can easily be extended to 3D, © 2006 IEEE.

A data collection and communication module for telemedicine and mhealth systems - Blank, Peter; Kugler, Patrick; Schuldhaus, Dominik; Eskofier, Björn (Proceedings of the 9th International Conference on Body Area Networks, 2014)
Wearable body sensors have become an important basis for today's medical and fitness applications. To assist athletes or to take care of elderly people in everyday life situations, sensor data can be collected and processed to give helpful feedback. However, the data collection process of multiple or different sensor systems still had to be done manually by the user or an expert, which usually takes a lot of time and can lead to errors. This paper presents an embedded data collection and communication module based on an AT90USB microcontroller, which can automatically acquire data from various wired and wireless sensors (e.g. via USB or Bluetooth® ). The obtained data can be cached, preprocessed and transmitted to a specified central server using LAN, Wi-Fi or GMS/GPRS. Through unified expansion slots, additional communication devices and a BeagleBoneTM embedded can be extended to handle many different sensor systems. Moreover, wired sensors can be charged through appropriate circuits. This data collection and communication module operates without any input settings and special knowledge by the user. Five prototypes with different configurations and extension units concerning communication interfaces and computation power have been built up. The evaluation of the transfer reliability with 100%, whereupon 98% of data could be transmitted at once and the remaining 2% with the next attempt, confirms the stability of data transmission.
UnivIS-Import:2016-06-01:Pub.2014.tech.IMMD.IMMD5.adatac
Keywords: Autonomous; BeagleBone; Data acquisition and transmission; Embedded computing; GSM/GPRS; mHealth; Microcontroller; Telemedicine; USB; Wearable sensors

Additively Manufactured Broadwall Waveguide Couplers for V-Band Applications - Hofmann, Andreas; Lomakin, Konstantin; Sippel, Mark; Gold, Gerald (German Microwave Conference, 2022)

Additively Manufactured Slotted Waveguides for THz Applications - Hofmann, Andreas; Lomakin, Konstantin; Sippel, Mark; Gold, Gerald (IEEE/MTT-S International Microwave Symposium (IMS), 2022)

Additively Manufactured WR15 Waveguide to Microstrip Transition for Broadband V-Band Applications - Hofmann, Andreas; Klein, Laura; Lomakin, Konstantin; Sippel, Mark; Gold, Gerald (European Microwave Conference (EuMC), 2022)

A deep learning approach to radiation dose estimation - Götz, Theresa; Schmidkonz, Christian; Chen, Shuqing; Al-Baddai, S.; Kuwert, Torsten; Lang, E. W. (Physics in Medicine and Biology, 2020)
Currently methods for predicting absorbed dose after administering a radiopharmaceutical are rather crude in daily clinical practice. Most importantly, individual tissue density distributions as well as local variations of the concentration of the radiopharmaceutical are commonly neglected. The current study proposes machine learning techniques like Green's function-based empirical mode decomposition and deep learning methods on U-net architectures in conjunction with soft tissue kernel Monte Carlo (MC) simulations to overcome current limitations in precision and reliability of dose estimations for clinical dosimetric applications. We present a hybrid method (DNN-EMD) based on deep neural networks (DNN) in combination with empirical mode decomposition (EMD) techniques. The algorithm receives x-ray computed tomography (CT) tissue density maps and dose maps, estimated according to the MIRD protocol, i.e. employing whole organ S-values and related time-integrated activities (TIAs), and from measured SPECT distributions of 177Lu radionuclei, and learns to predict individual absorbed dose distributions. In a second step, density maps are replaced by their intrinsic modes as deduced from an EMD analysis. The system is trained using individual full MC simulation results as reference. Data from a patient cohort of 26 subjects are reported in this study. The proposed methods were validated employing a leave-one-out cross-validation technique. Deviations of estimated dose from corresponding MC results corroborate a superior performance of the newly proposed hybrid DNN-EMD method compared to its related MIRD DVK dose calculation. Not only are the mean deviations much smaller with the new method, but also the related variances are much reduced. If intrinsic modes of the tissue density maps are input to the algorithm, variances become even further reduced though the mean deviations are less affected. The newly proposed hybrid DNN-EMD method for individualized radiation dose prediction outperforms the MIRD DVK dose calculation method. It is fast enough to be of use in daily clinical practice.
CRIS-Team Scopus Importer:2020-02-14

A deep learning based pipeline for optical coherence tomography angiography - Liu, Xi; Huang, Zhiyu; Wang, Zhenzhou; Wen, Chenyao; Jiang, Zhe; Yu, Zekuan; Liu, Jingfeng; Liu, Gangjun; Huang, Xiaolin; Maier, Andreas; Ren, Qiushi; Lu, Yanye (Journal of Biophotonics, 2019)
Optical coherence tomography angiography (OCTA) is a relatively new imaging modality that generates microvasculature map. Meanwhile, deep learning has been recently attracting considerable attention in image-to-image translation, such as image denoising, super-resolution and prediction. In this paper, we propose a deep learning based pipeline for OCTA. This pipeline consists of three parts: training data preparation, model learning and OCTA predicting using the trained model. To be mentioned, the datasets used in this work were automatically generated by a conventional system setup without any expert labeling. Promising results have been validated by in-vivo animal experiments, which demonstrate that deep learning is able to outperform traditional OCTA methods. The image quality is improved in not only higher signal-to-noise ratio but also better vasculature connectivity by laser speckle eliminating, showing potential in clinical use. Schematic description of the deep learning based optical coherent tomography angiography pipeline.
CRIS-Team Scopus Importer:2019-07-12
Keywords: CNN; deep learning; OCT angiography

A Digital Perfusion Phantom for T1-weighted DCE-MRI - Grimm, Robert; Court, Johannes; Fieselmann, Andreas; Block, Kai Tobias; Kiefer, Berthold; Hornegger, Joachim (Proceedings of International Society for Magnetic Resonance in Medicine, 2012)

A discriminative model-constrained em approach to 3D MRI brain tissue classification and intensity non-uniformity correction - Wels, Michael; Zheng, Yefeng; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin (Physics in Medicine and Biology, 2011)
We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets - consisting of 20 and 18 volumes, respectively - provided by the Internet Brain Segmentation Repository. © 2011 Institute of Physics and Engineering in Medicine.

A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI - Wels, Michael; Carneiro, Gustavo; Aplas, Alexander; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin (Springer-verlag, 2008)
In this paper we present a fully automated approach to the segmentation of pediatric brain tumors in multi-spectral 3-D magnetic resonance images. It is a top-down segmentation approach based on a Markov random field (MRF) model that combines probabilistic boosting trees (PBT) and lower-level segmentation via graph cuts. The PBT algorithm provides a strong discriminative observation model that classifies tumor appearance while a spatial prior takes into account the pair-wise homogeneity in terms of classification labels and multi-spectral voxel intensities. The discriminative model relies not only on observed local intensities but also on surrounding context for detecting candidate regions for pathology. A mathematically sound formulation for integrating the two approaches into a unified statistical framework is given. The proposed method is applied to the challenging task of detection and delineation of pediatric brain tumors. This segmentation task is characterized by a high non-uniformity of both the pathology and the surrounding non-pathologic brain tissue. A quantitative evaluation illustrates the robustness of the proposed method. Despite dealing with more complicated cases of pediatric brain tumors the results obtained are mostly better than those reported for current state-of-the-art approaches to 3-D MR brain tumor segmentation in adult patients. The entire processing of one multi-spectral data set does not require any user interaction, and takes less time than previously proposed methods. © 2008 Springer-Verlag Berlin Heidelberg.
UnivIS-Import:2015-04-16:Pub.2008.tech.IMMD.IMMD5.adiscr

A Divide-and-Conquer Approach towards Understanding Deep Networks - Fu, Weilin; Breininger, Katharina; Schaffert, Roman; Ravikumar, Nishant; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), 2019)
Deep neural networks have achieved tremendous success in various fields including medical image segmentation. However, they have long been criticized for being a black-box, in that interpretation, understanding and correcting architectures is difficult as there is no general theory for deep neural network design. Previously, precision learning was proposed to fuse deep architectures and traditional approaches. Deep networks constructed in this way benefit from the original known operator, have fewer parameters, and improved interpretability. However, they do not yield state-of-the-art performance in all applications. In this paper, we propose to analyze deep networks using known operators, by adopting a divide-and-conquer strategy to replace network components, whilst retaining networks performance. The task of retinal vessel segmentation is investigated for this purpose. We start with a high-performance U-Net and show by step-by-step conversion that we are able to divide the network into modules of known operators. The results indicate that a combination of a trainable guided filter and a trainable version of the Frangi filter yields a performance at the level of U-Net (AUC 0.974 vs. 0.972) with a tremendous reduction in parameters (111, 536 vs. 9, 575). In addition, the trained layers can be mapped back into their original algorithmic interpretation and analyzed using standard tools of signal processing. <
Keywords: Precision Learning; Debugging; CNN

Adult Cochlear Implant Users Versus Typical Hearing Persons: An Automatic Analysis of Acoustic–Prosodic Parameters - Arias-Vergara, Tomás; Batliner, Anton; Rader, Tobias; Polterauer, Daniel; Högerle, Catalina; Müller, Joachim; Orozco-Arroyave, Juan Rafael; Nöth, Elmar; Schuster, Maria (Journal of Speech Language and Hearing Research, 2022)
Purpose: The aim of this study was to investigate the speech prosody of post-lingually deaf cochlear implant (CI) users compared with control speakers without hearing or speech impairment. Method: Speech recordings of 74 CI users (37 males and 37 females) and 72 age-balanced control speakers (36 males and 36 females) are considered. All participants are German native speakers and read Der Nordwind und die Sonne (The North Wind and the Sun), a standard text in pathological speech analysis and phonetic transcriptions. Automatic acoustic analysis is performed considering pitch, loudness, and duration features, including speech rate and rhythm. Results: In general, duration and rhythm features differ between CI users and control speakers. CI users read slower and have a lower voiced segment ratio compared with control speakers. A lower voiced ratio goes along with a prolongation of the voiced segments’ duration in male and with a prolongation of pauses in female CI users. Rhythm features in CI users have higher variability in the duration of vowels and consonants than in control speakers. The use of bilateral CIs showed no advantages concerning speech prosody features in comparison to unilateral use of CI. Conclusions: Even after cochlear implantation and rehabilitation, the speech of postlingually deaf adults deviates from the speech of control speakers, which might be due to changed auditory feedback. We suggest considering changes in temporal aspects of speech in future rehabilitation strategies. Supplemental Material: https://doi.org/10.23641/asha.21579171.
CRIS-Team Scopus Importer:2022-12-23

Advanced 3-D Reconstruction of Talbot-Lau Data - Felsner, Lina (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2021)
X-ray imaging is a standard technique for examinations and interventions in medicine. Based on X-rays, Phase-Contrast Imaging (PCI) can provide an excellent soft-tissue contrast. The most promising setup for measuring phase-contrast in a medical context is the Talbot-Lau Interferometer (TLI), where specialized gratings are integrated between a regular medical X-ray tube and X-ray detector. The TLI allows to measure the classical X-ray attenuation in combination with the phase-contrast and dark-field signal. All three signals provide unique information and can in combination with each other enable an improved medical diagnostic. Acquiring volumetric scans with a TLI enables multi-modal imaging in which the reconstructed volumes are inherently registered. While medical procedures could greatly benefit from such a system, current implementations of such a setup in a medical environment are prevented by hardware- and software-based problems. In this work, we propose algorithms for solving algorithmic challenges towards the reconstruction of phase-contrast and dark-field signals with clinical compatibility. In the first part, advanced reconstruction techniques for the differential phase-contrast signal from a TLI are investigated. Here, the focus is on improving the reconstruction of the phase contrast data from a TLI for large objects. Current Talbot-Lau systems suffer from a small field of view, which leads to truncation for large objects, such as humans. To avoid artifacts in the reconstruction, we propose a method for phase-sensitive Region-of-Interest (ROI) Computed Tomography (CT). The proposal involves a special setup, where a large detector allows measuring complete non-truncated attenuation images, while additional gratings allow for phase-contrast imaging of a ROI. A corresponding correction algorithm is proposed to correct the truncation of the phase-contrast images. With experiments, we show that the proposed method can enable a clinically practical TLI implementation. Unfortunately, the signal strength of the phase measurement depends on the position of the object in setup. This leads to a change in the projection model and subsequent artifacts in the reconstruction. The artifacts prevent the evaluation of quantitative values and, therefore, the use of the data in a medical environment. In particular, this challenge introduces artifacts in the reconstruction of large objects. We propose two correction algorithms to alleviate this issue. The excellent performance of both algorithms in the experiments indicate the possibility for phase-sensitive CT for large objects. In the second part we consider the reconstruction of the dark-field signal. The scattering of the dark-field signal is high-dimensional, which leads to an orientation-dependent signal strength and prevents the use of Filtered Back-Projection (FBP) algorithms. Current projection models describe the signal formation only in two dimensions. Therefore, in order to reconstruct the dark-field, complex sampling trajectories must be used, which are clinically impractical. To tackle this challenge, we propose a general 3-D dark-field projection model, which enables to model the signal formation directly in 3-D. The general description facilitates a high degree of flexibility and enables modeling complex trajectories such as a helix. Our experimental evaluation shows a good agreement with wavefront simulations and real measurements. The projection model also allows a reconstruction of the dark-field signal with a true 3-D dark-field reconstruction algorithm. Experiments show the feasibility of a helical reconstruction and investigated the stability of the algorithm with respect to different influences. Overall, these promising first results indicate that a reconstruction of directed structures with a spiral trajectory is feasible.<br

Advanced Image Processing for Optical Coherence Tomography Angiography - Husvogt, Lennart; Maier, Andreas; Hornegger, Joachim (Proceedings of the 10th Joint Workshop of the German Research Training Groups in Computer Science, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.advanc

Advanced Real-time Indoor Parking Localization based on Semi-Static Objects - Groh, Benjamin; Friedl, Martin; Linarth, Andre Guilherme; Angelopoulou, Elli (Proceeding of 17th International Conference on Information Fusion, 2014)
Indoor parking localization systems face two major challenges: the absence of GPS coverage and the dynamic character of the parking environment. The absence of GPS coverage can be compensated by data fusion of several sensors in order to localize an object in a map of the environment. However, this map may constantly change due to the dynamic nature of a parking garage. This paper describes an improvement to indoor localizations by a new map representation approach. The algorithm is developed for the application of an indoor parking system. The car park's map is based on the known permanent elements of the environment in combination with the knowledge of possibly changing elements. The permanent elements (e.g. walls) are considered static objects, while the known changing elements (e.g. parked cars) are modeled as semi-static objects. Such a representation avoids constantly updating the map with newly detected objects. Our algorithm fulfills the real-time requirement of the localization task for a computer-controlled car through the use of a particle filter. The filter uses laser scanner measurements and odometry data to localize the car in a precalculated probability grid map, containing static and semi-static elements. The evaluation of the algorithm in a real car park scenario demonstrates the robustness and real-time capability of the proposed system with a RMS error in the absolute position of 0.33m and in the heading angle of 1.03° at a computation time of 10ms per cycle.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.advanc

Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction - Seitzer, Maximilian; Yang, Guang; Schlemper, Jo; Oktay, Ozan; Würfl, Tobias; Christlein, Vincent; Wong, Tom; Mohiaddin, Raad; Firmin, David; Keegan, Jennifer; Rueckert, Daniel; Maier, Andreas (MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT I, 2018)
CRIS-Team WoS Importer:2019-08-13

A dynamic reconstruction approach for cerebral blood flow quantification with an interventional C-arm CT - Fieselmann, Andreas; Ganguly, Arundhuti; Deuerling-Zheng, Yu; Zellerhoff, Maria; Boese, Jan; Hornegger, Joachim; Fahrig, Rebecca (7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010, 2010)
Tomographic perfusion imaging is a well accepted method for stroke diagnosis that is available with current CT and MRI scanners. A challenging new method, which is currently not available, is perfusion imaging with an interventional C-arm CT that can acquire 4-D images using a C-arm angiography system. This method may help to optimize the workflow during catheter-guided stroke treatment. The main challenge in perfusion C-arm CT is the comparably slow rotational speed of the C-arm (approximately 5 seconds) which decreases the overall temporal resolution. In this work we present a dynamic reconstruction approach optimized for perfusion C-arm CT based on temporal estimation of partially backprojected volumes. We use numerical simulations to validate the algorithm: For a typical configuration the relative error in estimated arterial peak enhancement decreases from 14.6% to 10.5% using the dynamic reconstruction. Furthermore we present initial results obtained with a clinical C-arm CT in a pig model. © 2010 IEEE.

A factorization approach for cone-beam reconstruction on a circular short-scan - Dennerlein, Frank; Noo, Frederic; Schoendube, Harald; Lauritsch, Günter; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2008)
In this paper, we introduce a new algorithm for 3-D image reconstruction from cone-beam (CB) projections acquired along a partial circular scan. Our algorithm is based on a novel, exact factorization of the initial 3-D reconstruction problem into a set of independent 2-D inversion problems, each of which corresponds to finding the object density on one, single plane. Any such 2-D inversion problem is solved numerically using a projected steepest descent iteration scheme. We present a numerical evaluation of our factorization algorithm using computer-simulated CB data, without and with noise, of the FORBILD head phantom and of a disk phantom. First, we study quantitatively the impact of the reconstruction parameters on the algorithm performance. Next, we present reconstruction results for visual assessment of the achievable image quality and provide, for comparison, results obtained with two other state-of-the-art reconstruction algorithms for the circular short-scan. © 2006 IEEE.

A Fair Evaluation of Various Deep Learning-Based Document Image Binarization Approaches - Sukesh, Richin; Seuret, Mathias; Nicolaou, Anguelos; Mayr, Martin; Christlein, Vincent (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
Binarization of document images is an important pre-processing step in the field of document analysis. Traditional image binarization techniques usually rely on histograms or local statistics to identify a valid threshold to differentiate between different aspects of the image. Deep learning techniques are able to generate binarized versions of the images by learning context-dependent features that are less error-prone to degradation typically occurring in document images. In recent years, many deep learning-based methods have been developed for document binarization. But which one to choose? There have been no studies that compare these methods rigorously. Therefore, this work focuses on the evaluation of different deep learning-based methods under the same evaluation protocol. We evaluate them on different Document Image Binarization Contest (DIBCO) datasets and obtain very heterogeneous results. We show that the DE-GAN model was able to perform better compared to other models when evaluated on the DIBCO2013 dataset while DP-LinkNet performed best on the DIBCO2017 dataset. The 2-StageGAN performed best on the DIBCO2018 dataset while SauvolaNet outperformed the others on the DIBCO2019 challenge. Finally, we make the code, all models and evaluation publicly available (https://github.com/RichSu95/Document_Binarization_Collection ) to ensure reproducibility and simplify future binarization evaluations.
CRIS-Team Scopus Importer:2022-06-10
Keywords: Binarization; Deep learning; Evaluation

A Feasibility Study of Automatic Multi-Organ Segmentation Using Probabilistic Atlas - Chen, Shuqing; Endres, Jürgen; Dorn, Sabrina; Maier, Joscha; Lell, Michael; Kachelriess, Marc; Maier, Andreas (Bildverarbeitung für die Medizin: Algorithmen-Systeme-Anwendungen., 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.afeasi

Affective and behavioural computing: Lessons learnt from the First Computational Paralinguistics Challenge - Schuller, Bjoern; Weninger, Felix; Zhang, Yue; Ringeval, Fabien; Batliner, Anton; Steidl, Stefan; Eyben, Florian; Marchi, Erik; Vinciarelli, Alessandro; Scherer, Klaus; Chetouani, Mohamed; Mortillaro, Marcello (Computer Speech and Language, 2019)
In this article, we review the INTERSPEECH 2013 Computational Paralinguistics ChallengE (ComParE) – the first of its kind – in light of the recent developments in affective and behavioural computing. The impact of the first ComParE instalment is manifold: first, it featured various new recognition tasks including social signals such as laughter and fillers, conflict in dyadic group discussions, and atypical communication due to pervasive developmental disorders, as well as enacted emotion; second, it marked the onset of the ComParE, subsuming all tasks investigated hitherto within the realm of computational paralinguistics; finally, besides providing a unified test-bed under well-defined and strictly comparable conditions, we present the definite feature vector used for computation of the baselines, thus laying the foundation for a successful series of follow-up Challenges. Starting with a review of the preceding INTERSPEECH Challenges, we present the four Sub-Challenges of ComParE 2013. In particular, we provide details of the Challenge databases and a meta-analysis by conducting experiments of logistic regression on single features and evaluating the performances achieved by the participants.
CRIS-Team Scopus Importer:2019-05-02
Keywords: Autism; Challenge; Computational Paralinguistics; Conflict; Emotion; Social Signals; Survey

A filter model to analyze reconstruction artifacts in perfusion C-arm CT - Fieselmann, Andreas; Dennerlein, Frank; Deuerling-Zheng, Yu; Boese, Jan; Fahrig, Rebecca; Hornegger, Joachim (2010 IEEE Nuclear Science Symposium, Medical Imaging Conference, NSS/MIC 2010 and 17th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, RTSD 2010, 2010)
Interventional perfusion imaging with a C-arm angiography system capable of 3-D imaging (C-arm CT) could optimize the clinical workflow during stroke treatment. Acquisition times are currently of the order of several seconds per shortscan and filtered backprojection reconstruction artifacts can arise due to the time-dependence of the attenuation values. In this paper, we present a novel formalism to model reconstruction artifacts by a spatio-temporal filter in the object domain. The filter consists of terms that depend on the temporal derivatives of the attenuation values. Using this model, artifacts due to time-dependent attenuation values can be well explained and studied. We apply the filter model to analyze redundancy weighting schemes in order to reduce these artifacts. Furthermore, this model could be the basis to develop new dynamic reconstruction algorithms. © 2010 IEEE.

AFiT - Atrial Fibrillation Ablation Planning Tool - Brost, Alexander; Bourier, Felix; Kleinöder, Andreas; Raab, Jens; Koch, Martin; Stamminger, Marc; Hornegger, Joachim; Strobel, Norbert; Kurzidim, Klaus (VMV, 2011)

A Frame and Segment Based Approach for Topic Spotting - Nöth, Elmar; Harbeck, Stefan; Niemann, Heinrich; Warnke, Volker (Proc. European Conf. on Speech Communication and Technology, 1997)

A framework for statistical 3-D object recognition - Paulus, Dietrich; Hornegger, Joachim; Niemann, Heinrich (Pattern Recognition Letters, 1997)
In this contribution we describe an object-oriented software architecture for image segmentation, 3-D pose estimation as well as Bayesian object recognition: models are represented by densities, model generation corresponds to parameter estimation tasks, and the identification applies the Bayesian decision rule. We show results of 3-D object recognition experiments based on the observation of 2-D points or lines. © 1997 Elsevier Science B.V.

A framework for voxel-based morphometric analysis of the optic radiation using diffusion tensor imaging in glaucoma - El-Rafei, Ahmed Mohamed Ibrahim; Engelhorn, Tobias; Wärntges, Simone; Dörfler, Arnd; Hornegger, Joachim; Michelson, Georg (Magnetic Resonance Imaging, 2011)
EVALuna2:21060

A full multigrid technique to accelerate an ART scheme for tomographic image reconstruction - Prümmer, Marcus; Köstler, Harald; Hornegger, Joachim; Rüde, Ulrich (Frontiers in Simulation, 2005)
Tomographic reconstruction is the process of reconstructing a 3-D object or its cross section from several of its 2-D projection images. The object is illuminated by a cone-beam of Xrays, where the signal is attenuated by the object. Due to its speed filtered back projection (FBP) still is state-of-the-art in 3-D reconstruction for clinical use where time matters. But considering the accuracy and number of projections required for FBP, as shown in [1], an algebraic reconstruction technique (ART) is superior. Our current focus lies on 3-D angiography using C-arm systems. But this new approach should also be applicable on many real world reconstruction problems. Within ART, the object is represented as a linear combination of basis functions, typically voxels, with some unknown coefficients. The observations can also be expressed as a linear combination of these coefficients. This results in a linear system of equations with a sparse system matrix, because each X-ray intensity observation is influenced only by the pixels on the corresponding beam path. If enough measures are available, one has an over-determined system, which is solved in the leastsquares sense. On the other hand, if there are not enough measures in a region to determine the coefficient values, one is faced with an under-determined problem. In this case, one solves the regularized version of the problem which supplies the additional constraints. Due to the large number of unknowns in real applications, an iterative instead of a direct linear solver has to be used. Techniques such as Kaczmarz’s algorithm or CAV (component averaging) are currently used as iterative solvers, but for large problems, their computational costs are high. In addition, these solvers tend to improve the solution very much only in the first few iterations. An efficient ART is therefore essential to compete with FBP successfully. In this paper we think of these iterative methods as smoothers within a multigrid solver. It should be noted that because of the structure of the system matrix, the standard multigrid theory is not applicable here. The additional ingredients of the multigrid method are coarser versions of the problem on different levels, interpolation and restriction operators. For the coarser problems, we uniformly reduce the number of rays and the number of voxels while keeping the overall volume constant. Furthermore, we use trilinear interpolation and full weighting as restriction. Full multigrid is then accomplished by starting on each level the V-cycle with an initial guess for the solution that is interpolated from the next coarser level. Our experiments show that we are able to reduce the relative error to a certain size by less Kaczmarz smoothing steps on the finest level when using the multigrid method instead of the common Kaczmarz algorithm. We present results for real medical datasets and compare our multigrid method with Kaczmarz and CAV on a phantom. One of the next steps will be to detail the theory for our multigrid method in order to get estimates for the asymptotic convergence rates.

A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography - Bögel, Marco; Hölter, Philip; Redel, Thomas; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015)
Subarachnoid hemorrhage due to a ruptured cerebral aneurysm is still a devastating disease. Planning of endovascular aneurysm therapy is increasingly based on hemodynamic simulations necessitating reliable vessel segmentation and accurate assessment of vessel diameters. In this work, we propose a fully-automatic, locally adaptive, gradient-based thresholding algorithm. Our approach consists of two steps. First, we estimate the parameters of a global thresholding algorithm using an iterative process. Then, a locally adaptive version of the approach is applied using the estimated parameters. We evaluated both methods on 8 clinical 3D DSA cases. Additionally, we propose a way to select a reference segmentation based on 2D DSA measurements. For large vessels such as the internal carotid artery, our results show very high sensitivity (97.4%), precision (98.7%) and Dice-coefficient (98.0%) with our reference segmentation. Similar results (sensitivity: 95.7%, precision: 88.9% and Dice-coefficient: 90.7%) are achieved for smaller vessels of approximately 1mm diameter.
CRIS-Team Scopus Importer:2022-08-05

A Gauss-Seidel Iteration Scheme for Reference-Free 3-D Histological Image Reconstruction - Gaffling, Simone; Daum, Volker; Steidl, Stefan; Maier, Andreas; Köstler, Harald; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2015)
Three-dimensional (3-D) reconstruction of histological slice sequences offers great benefits in the investigation of different morphologies. It features very high-resolution which is still unmatched by in vivo 3-D imaging modalities, and tissue staining further enhances visibility and contrast. One important step during reconstruction is the reversal of slice deformations introduced during histological slice preparation, a process also called image unwarping. Most methods use an external reference, or rely on conservative stopping criteria during the unwarping optimization to prevent straightening of naturally curved morphology. Our approach shows that the problem of unwarping is based on the superposition of low-frequency anatomy and high-frequency errors. We present an iterative scheme that transfers the ideas of the Gauss-Seidel method to image stacks to separate the anatomy from the deformation. In particular, the scheme is universally applicable without restriction to a specific unwarping method, and uses no external reference. The deformation artifacts are effectively reduced in the resulting histology volumes, while the natural curvature of the anatomy is preserved. The validity of our method is shown on synthetic data, simulated histology data using a CT data set and real histology data. In the case of the simulated histology where the ground truth was known, the mean Target Registration Error (TRE) between the unwarped and original volume could be reduced to less than 1 pixel on average after six iterations of our proposed method.
UnivIS-Import:2015-03-09:Pub.2015.tech.IMMD.IMMD5.agauss
Keywords: 3-D histology; Gauss-Seidel; reconstruction; reference-free; registration

Age and Gender Recognition for Telephone Applications Based on GMM Supervectors and Support Vector Machines - Bocklet, Tobias; Maier, Andreas; Bauer, Josef; Burkhardt, Felix; Nöth, Elmar (Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2008)

Age Determination of Children in Preschool and Primary School Age with GMM-Based Supervectors and Support Vector Machines/Regression - Bocklet, Tobias; Maier, Andreas; Nöth, Elmar (Proceedings Text, Speech and Dialogue; 11th International Conference, 2008)

Age Estimation on Panoramic Dental X-ray Images using Deep Learning - Wallraff, Sarah; Vesal, Sulaiman; Syben-Leisner, Christopher; Lutz, Rainer; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

A General View on Timetabling Problems - Gröbner, Matthias; Wilke, Peter (Proc. 4th Int. Conference on the Practice and Theory of Automated Timetabling IV, 2003)
UnivIS-Import:2017-07-10:Pub.2003.tech.IMMD.IMMD5.agener
Keywords: timetabling

A generic probabilistic active shape model for organ segmentation - Wimmer, Andreas; Soza, Grzegorz; Hornegger, Joachim (Springer-verlag, 2009)
Probabilistic models are extensively used in medical image segmentation. Most of them employ parametric representations of densities and make idealizing assumptions, e.g. normal distribution of data. Often, such assumptions are inadequate and limit a broader application. We propose here a novel probabilistic active shape model for organ segmentation, which is entirely built upon non-parametric density estimates. In particular, a nearest neighbor boundary appearance model is complemented by a cascade of boosted classifiers for region information and combined with a shape model based on Parzen density estimation. Image and shape terms are integrated into a single level set equation. Our approach has been evaluated for 3-D liver segmentation using a public data base originating from a competition (http://sliver07.org). With an average surface distance of 1.0 mm and an average volume overlap error of 6.5 %, it outperforms other automatic methods and provides accuracy close to interactive ones. Since no adaptions specific to liver segmentation have been made, our probabilistic active shape model can be applied to other segmentation tasks easily. © 2009 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD5.agener
Keywords: Segmentation; Active Shape Model; Level Set; Probabilistic; Liver

A gentle introduction to deep learning in medical image processing [Eine sanfte Einführung in Tiefes Lernen in der Medizinischen Bildverarbeitung] - Maier, Andreas; Syben-Leisner, Christopher; Lasser, Tobias; Riess, Christian (Zeitschrift für Medizinische Physik, 2019)
This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modeling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.
CRIS-Team Scopus Importer:2019-05-03
Keywords: Computer-aided diagnosis; Deep learning; Image reconstruction; Image registration; Image segmentation; Introduction; Machine learning; Physical simulation

A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging - Xiong, Zhaohan; Xia, Qing; Hu, Zhiqiang; Huang, Ning; Bian, Cheng; Zheng, Yefeng; Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas; Yang, Xin; Heng, Pheng Ann; Ni, Dong; et al. (Medical Image Analysis, 2021)
Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community.
CRIS-Team Scopus Importer:2020-11-13
Keywords: Convolutional neural networks; Image segmentation; Late gadolinium-enhanced magnetic resonance imaging; Left atrium

A Guided Spatial Transformer Network for Histology Cell Differentiation - Aubreville, Marc; Krappmann, Maximilian; Bertram, Christof; Klopfleisch, Robert; Maier, Andreas (Eurographics Workshop on Visual Computing for Biology and Medicine, 2017)
Identification and counting of cells and mitotic figures is a standard task in diagnostic histopathology. Due to the large overall cell count on histological slides and the potential sparse prevalence of some relevant cell types or mitotic figures, retrieving annotation data for sufficient statistics is a tedious task and prone to a significant error in assessment. Automatic classification and segmentation is a classic task in digital pathology, yet it is not solved to a sufficient degree. We present a novel approach for cell and mitotic figure classification, based on a deep convolutional network with an incorporated Spatial Transformer Network. The network was trained on a novel data set with ten thousand mitotic figures, about ten times more than previous data sets. The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image. The mean accuracy of the algorithm in a five-fold cross-validation is 91.45 %. In our view, the approach is a promising step into the direction of a more objective and accurate, semi-automatized mitosis counting supporting the pathologis

A human face detection system based on Schneiderman-Kanade method - Setyawan, Iwan; Wibowo, Yohan; Timotius, Ivanna; Febrianto, Andreas A. (2011 6th International Conference on Telecommunication Systems, Services, and Applications (TSSA), 2011)
Face detection is a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings, trees and other parts of the body. In this paper, we present a face detection system based on the Schneiderman-Kanade method. This system is trained using visual attributes extracted from training samples.

A Hybrid Approach for Simultaneous Obstacle Avoidance and Stabilization of Dynamic Bipedal Walking using the Aldebaran Nao Robot - Ullrich, Christian; Deitsch, Sergiu; Gallwitz, Florian (15th International Conference on Innovations for Community Services (I4CS), 2015)
In this paper, we propose a monocular vision system for autonomous obstacle avoidance and simultaneous stabilization of dynamic bipedal walking using the Aldebaran Nao robot. In particular, we address the case where erroneous bipedal locomotion causes the robot to drift away from a planned trajectory over time. To eliminate drifting, we use hybrid control patterns. This results in an efficient, self-correcting system that does not require to alter the robot's world representation. We confirm the efficiency and accuracy of the proposed system in a set of experiments.
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.ahybri
Keywords: Cameras; Collision avoidance; Computer architecture; Legged locomotion; Navigation; GRK-1773

A Hybrid Approach for Virtual Clinical Trials for Mammographic Imaging - Schebesch, Frank; Magdalena, Herbst; Mertelmeier, Thomas; Maier, Andreas; Ritschl, Ludwig (Proc. of SPIE, 2018)
Detection of lesions is an essential part of making a diagnosis in mammography and therefore is a main focus in the development of algorithms built for image quality assessment. We propose a hybrid approach with an accurate lesion projection model and embedding of lesions into clinical images that already contain relevant structures of anatomical noise. Using an algebraic lesion model, lesions with different sizes and contrasts are generated. The projection algorithm incorporates the modeling of blur effects due to system movement and the physical extent of the anode. Signal and background patches are extracted and used to evaluate channelized Hotelling observers with Laguerre-Gauss channels and with Gabor channels. A four-alternative forced-choice study with five medical imaging experts is performed and the inter-reader agreement with and without the model observers is determined by using Fleiss' kappa. Analyzing three different sizes for tiny, dense lesions and four density levels for larger mass-like lesions we find a good detection rate of the tiny lesions for both human as well as model observers. The inter-reader agreement using the common interpretation of Fleiss' kappa is substantial or better. Comparing full-field digital mammography and digital breast tomosynthesis w.r.t. the different mass densities we find that human readers and model observers perform well on the DBT data and the detection rate drops with lesion contrast as expected. The inter-reader agreement here is fair for the lowest contrast and substantial for the denser cases. Both human readers and model observers show difficulty in detecting the low contrast lesions in FFDM images. The inter-reader agreement is rather poor among all readers. Overall, the results indicate a good agreement between human observers and model observers and a distinctive benefit of 3-D reconstruction over FFDMs for low contrast lesions.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.ahybri_2
Keywords: model observer; digital breast tomosynthesis; mammography; image quality; lesion detection

AI Makes Medicine More Efficient, Individual and Preventive - Hornegger, Joachim (Springer Fachmedien Wiesbaden, 2023)
Artificial intelligence has the potential to fundamentally transform medicine. Even as of today, AI programs show that they can outperform doctors in the evaluation of medical imaging data. Sensor-based monitoring in combination with self-learning algorithms shifts the focus increasingly from the clinic to the home environment, from therapy to prevention. The systematic analysis of structured information using data mining methods provides new insights into the causes of diseases and the success of medical interventions and therapies. The key will be how information is integrated in the future and how the individual retains sovereignty over his or her data.
CRIS-Team Scopus Importer:2023-09-29

A Kernel-based Framework for Intra-fractional Respiratory Motion Estimation in Radiation Therapy - Geimer, Tobias; Unberath, Mathias; Birlutiu, Adriana; Taubmann, Oliver; Wölfelschneider, Jens; Bert, Christoph; Maier, Andreas (Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging, 2017)
In radiation therapy, tumor tracking allows to adjust the beam such that it follows the respiration-induced tumor motion. However, most clinical approaches rely on implanted fiducial markers to locate the tumor and, thus, only provide sparse information. Motion models have been investigated to estimate dense internal displacement fields from an external surrogate signal, such as range imaging. With increasing surrogate complexity, we propose a respiratory motion estimation framework based on kernel ridge regression to cope with high-dimensional domains. This approach was validated on five patient datasets, consisting of a planning 4DCT and a follow-up 4DCT for each patient. Mean residual error was at best 2.73 ± 0.25 mm, but varied greatly between patients.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.akerne_4
Keywords: Motion Models; Range Imaging; Radiation Therapy; Kernel Trick; Active Shape Models

A Kernel Ridge Regression Model for Respiratory Motion Estimation in Radiotherapy - Geimer, Tobias; Birlutiu, Adriana; Unberath, Mathias; Taubmann, Oliver; Bert, Christoph; Maier, Andreas (Bildverarbeitung für die Medizin 2017 - Algorithmen - Systeme - Anwendungen, 2017)
This paper discusses a kernel ridge regression (KRR) model for motion estimation in radiotherapy. Using KRR, dense internal motion fields are estimated from high-dimensional surrogates without the need for prior dimensionality reduction. We compare the proposed model to a related approach with dimensionality reduction in the form of principal component analysis and principle component regression. Evaluation was performed in a simulation study based on nine 4D CT patient data sets achieving a mean estimation error of 0.84 ± 0.21 mm for our approach.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.akerne_3

Akustische automatische Erkennung von Sigmatismus bei Kindern - Bocklet, Tobias; Valentini Botinhao, Cassia; Degenkolb-Weyers, Sabine; Nöth, Elmar; Eysholdt, Ulrich (Aktuelle phoniatrisch-pädaudiologische Aspekte, 2011)

Alae Tracker: Tracking of the Nasal Walls in MR-Imaging - Breininger, Katharina; Maier, Andreas; Forman, Christoph; Flatz, Wilhelm; Messmer, Catalina; Schuster, Maria (Bildverarbeitung für die Medizin 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.alaetr
Keywords: tracking; nose; MRI

A language-independent feature set for the automatic evaluation of prosody - Maier, Andreas; Hönig, Florian Thomas; Zeißler, Viktor; Batliner, Anton; Körner, Erik; Yamanaka, Nobuyuki; Ackermann, Peter Donald; Nöth, Elmar (Proceedings of the 10th Annual Conference of the International Speech Communication Association (Interspeech 2009), 2009)

A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor - Bertram, Christof A.; Aubreville, Marc; Marzahl, Christian; Maier, Andreas; Klopfleisch, Robert (Scientific Data, 2019)
We introduce a novel, large-scale dataset for microscopy cell annotations. The dataset includes 32 whole slide images (WSI) of canine cutaneous mast cell tumors, selected to include both low grade cases as well as high grade cases. The slides have been completely annotated for mitotic figures and we provide secondary annotations for neoplastic mast cells, inflammatory granulocytes, and mitotic figure look-alikes. Additionally to a blinded two-expert manual annotation with consensus, we provide an algorithm-aided dataset, where potentially missed mitotic figures were detected by a deep neural network and subsequently assessed by two human experts. We included 262,481 annotations in total, out of which 44,880 represent mitotic figures. For algorithmic validation, we used a customized RetinaNet approach, followed by a cell classification network. We find F1-Scores of 0.786 and 0.820 for the manually labelled and the algorithm-aided dataset, respectively. The dataset provides, for the first time, WSIs completely annotated for mitotic figures and thus enables assessment of mitosis detection algorithms on complete WSIs as well as region of interest detection algorithms.
Keywords: mitotic figure detection; tumor grading; digital pathology

A learning-based material decomposition pipeline for multi-energy x-ray imaging - Lu, Yanye; Kowarschik, Markus; Huang, Xiaolin; Xia, Yan; Choi, Jang-Hwan; Chen, Shuqing; Hu, Shiyang; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (Medical Physics, 2019)
PurposeBenefiting from multi-energy x-ray imaging technology, material decomposition facilitates the characterization of different materials in x-ray imaging. However, the performance of material decomposition is limited by the accuracy of the decomposition model. Due to the presence of nonideal effects in x-ray imaging systems, it is difficult to explicitly build the imaging system models for material decomposition. As an alternative, this paper explores the feasibility of using machine learning approaches for material decomposition tasks.
CRIS-Team WoS Importer:2019-03-12

Algebraic Full Multigrid in Image Reconstruction - Köstler, Harald; Popa, Constantin; Prümmer, Marcus; Rüde, Ulrich (Series on Mathematical Modelling of Environmental and Life Sciences, Proceedings of the fifth workshop, September, 2006, Constanta, Romania, 2008)
In this paper we propose an algebraic full multigrid algorithm for ef- ficient and robust numerical solution of arbitrary linear systems of equations arising in image reconstruction from projections in computerized tomography. Numerical experiments and comparisons with the classical Kaczmarz algebraic reconstruction technique are presented.
UnivIS-Import:2015-04-16:Pub.2008.tech.IMMD.lsinfs.algebr

Algorithmus zur IR-Panoramabilderstellung aus IR-Luftaufnahmen von PV-Freiflächenanlagen - Dalsaß, Manuel; Deitsch, Sergiu; Moerman, Daniil; Gallwitz, Florian; Brabec, Christoph (32. Symposium Photovoltaische Solarenergie, 2017)

Alignment of multiple Time-of-Flight 3D Cameras for Reconstruction of walking feet - Stürmer, Michael; Seiler, Claude; Becker, Guido; Hornegger, Joachim (ISB2011 Brussels, Conference book Program & Abstracts, 2011)

A linear programming approach to limited angle 3d reconstruction from dsa- projections - Weber, Stefan; Schüle, Thomas; Schnörr, Christoph; Hornegger, Joachim (Bildverarbeitung für die Medizin 2003, 2003)

A linear programming approach to limited angle 3D recostruction from DSA projections - Weber, Stefan; Schüle, Thomas; Schnörr, Christoph; Hornegger, Joachim (Methods of Information in Medicine, 2004)
Objectives: We investigate the feasibility of binary-valued 3D tomographic reconstruction using only a small number of projections acquired over a limited range of angles. Methods: Regularization of this strongly ill-posed problem is achieved by (i) confining the reconstruction to binary vessel/non-vessel decisions, and (ii) by minimizing a global functional involving a smoothness prior. Results: Our approach successfully reconstructs volumetric vessel structures from three projections taken within 90°. The percentage of reconstructed voxels differing from ground truth is below 1%. Conclusion: We demonstrate that for particular applications - like Digital Subtraction Angiography - 3D reconstructions are possible where conventional methods must fail, due to a severely limited imaging geometry. This could play an important role for dose reduction and 3D reconstruction using non-conventional technical setups.
UnivIS-Import:2015-03-09:Pub.2004.tech.IMMD.IMMD5.alinea

A linear programming relaxation for binary tomography with smoothness priors - Weber, Stefan; Schnörr, Christoph; Hornegger, Joachim (Proc. of 9th International Workshop on Combinatorial Image Analysis, 2003)

A linear programming relaxation for binary tomography with smoothness priors - Weber, Stefan; Schnörr, Christoph; Hornegger, Joachim (2002)
UnivIS-Import:2016-06-30:Pub.2002.tech.IMMD.IMMD5.alinea

Alzheimer's Detection from English to Spanish Using Acoustic and Linguistic Embeddings - Pérez Toro, Paula Andrea; Klumpp, Philipp; Hernandez, Abner; Arias Vergara, Tomás; Lillo, P.; Slachevsky, A.; García, A. M.; Schuster, M.; Maier, Andreas; Nöth, Elmar; Orozco Arroyave, Juan Rafael (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2022)
Cross-lingual approaches are growing in popularity in the machine learning domain, where large amounts of data are required to obtain better generalizations. Moreover, one of the biggest problems is the availability of clinical speech data, where most of the resources are in English. For instance, not many available Alzheimer's Disease (AD) corpora in different languages can be found in the literature. Despite the phonological and phonemic differences between Spanish and English, fortunately, there are also similarities between these two languages, e.g., around 40% of all words in English have a related word in Spanish. In this work, we want to investigate the feasibility of combining information from English and Spanish languages to discriminate AD. Two datasets were considered: part of the Pitt Corpus, which is composed of English speakers, and a Spanish AD dataset composed of speakers from Chile. We based our analysis on known acoustic (Wav2Vec) and word (BERT, RoBERTa) embeddings using different classifiers. Strong language dependencies were found, even using multilingual representations. We observed that linguistic information was more important for classifying English AD (F-Score=0.76) and acoustic for Spanish AD (F-Score=0.80). Using knowledge transferred from English to Spanish achieved F-scores of up to 0.85 for discriminating AD.
CRIS-Team Scopus Importer:2022-10-28
Keywords: Acoustic Embeddings; Alzheimer's Disease; Cross-Lingual Analysis; Linguistic Embeddings

A machine learning perspective on the emotional content of Parkinsonian speech - Sechidis, Konstantinos; Fusaroli, Riccardo; Orozco Arroyave, Juan Rafael; Wolf, Detlef; Zhang, Yan Ping (Artificial Intelligence in Medicine, 2021)
Patients with Parkinson's disease (PD) have distinctive voice patterns, often perceived as expressing sad emotion. While this characteristic of Parkinsonian speech has been supported through the perspective of listeners, where both PD and healthy control (HC) subjects repeat the same speaking tasks, it has never been explored through a machine learning modelling approach. Our work provides an objective evaluation of this characteristic of the PD speech, by building a transfer learning system to assess how the PD pathology affects the sadness perception. To do so we introduce a Mixture-of-Experts (MoE) architecture for speech emotion recognition designed to be transferable across datasets. Firstly, by relying on publicly available emotional speech corpora, we train the MoE model and then we use it to quantify perceived sadness in never seen before PD and matched HC speech recordings. To build our models (experts), we extracted spectral features of the voicing parts of speech and we trained a gradient boosting decision trees model in each corpus to predict happiness vs. sadness. MoE predictions are created by weighting each expert's prediction according to the distance between the new sample and the expert-specific training samples. The MoE approach systematically infers more negative emotional characteristics in PD speech than in HC. Crucially, these judgments are related to the disease severity and the severity of speech impairment in the PD patients: the more impairment, the more likely the speech is to be judged as sad. Our findings pave the way towards a better understanding of the characteristics of PD speech and show how publicly available datasets can be used to train models that provide interesting insights on clinical data.
CRIS-Team Scopus Importer:2021-04-30
Keywords: Machine learning; Mixture-of-experts; Parkinson's disease; Speech emotion recognition

A Memory Management Library for CT-Reconstruction on GPUs - Wu, Hao; Berger, Martin; Maier, Andreas; Lohmann, Daniel (Bildverarbeitung für die Medizin, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.amemor
Keywords: GRK-1773

A Microphone-independent Visualization Technique for Speech Disorders - Maier, Andreas; Wenhardt, Stefan; Haderlein, Tino; Schuster, Maria; Nöth, Elmar (Proceedings of the 10th Annual Conference of the International Speech Communication Association (Interspeech 2009), 2009)

A Mixed Reality Simulation for Robotic Systems - Leipert, Martin; Sadowski, Jenny; Kießling, Michèle; Ngandeu, Emeric Kwemou; Maier, Andreas (Informatik aktuell, 2019)
In interventional angiography, kinematic simulation of robotic system prototypes in early development phases facilitates the detection of design errors. In this work, a game engine visualization with output is developed for such a robotic simulation. The goal of this is a better perception of the prototype by more realistic visualization. The achieved realism is evaluated in a user study. Additionally, the inclusion of real rooms walls into the simulations collision model is tested and evaluated, to verify smartglasses as a tool for interactive room planning. The walls are reconstructed from point clouds using a mean shift segmentation and RANSAC. Afterwards, the obtained wall estimates are ordered using a simple neighborhood graph.
CRIS-Team Scopus Importer:2021-04-20

A model for filtered backprojection reconstruction artifacts due to time-varying attenuation values in perfusion C-arm CT - Fieselmann, Andreas; Dennerlein, Frank; Deuerling-Zheng, Yu; Boese, Jan; Fahrig, Rebecca; Hornegger, Joachim (Physics in Medicine and Biology, 2011)

A Multilingual Prosody Module in a Speech-to-Speech Translation System - Nöth, Elmar; Batliner, Anton; Buckow, Jan-Constantin; Huber, Richard; Warnke, Volker; Niemann, Heinrich (Proc. of the Workshop on Multi-Lingual Speech Communication, 2000)

A Multi-Stage Recognition System to Detect Different Types of Abnormality in Capsule Endoscope Images - Miaou, Shaou-Gang; Chang, Feng-Ling; Timotius, Ivanna; Huang, Han-Chang; Su, Jenn-Lung; Liao, Rung-Sheng; Lin, Tah-Yeong (Journal of Medical and Biological Engineering, 2009)
The capsule endoscope is a state-of-the-art tool to detect abnormal problems of the small intestines, such as chyme blockage, suspected blood indicator (SBI), and white spots (ulcer). However, each examination using a capsule endoscope produces several tens of thousands images, and it is time-consuming for a physician to examine all the images. This paper proposes an automatic recognition system to identify suspected capsule endoscope images to save image viewing time. The system is basically a four-stage classifier. The first stage uses the hue, saturation, and intensity (HSI) color model to find the images with large yellow-green abnormal areas (could be chyme-blocked). For those not selected in the first stage, the second stage uses fuzzy c-means clustering analysis to further recognize the images with large abnormal areas (could be SBI). Most of the images encountered at the third stage have either large normal and uniform areas, or small abnormal areas. Thus, this stage attempts to exclude uniform images which are likely to be normal. Finally, the last stage uses a back-propagation neural network to detect the images with small abnormal areas (could be white spots). Experimental results showed that this cascaded classification system could perform much better than its individual stages or some combinations of the stages. The overall recognition accuracy is about 89%, resulting in unavoidable misclassified images. However, the detection sensitivity for abnormal images by the system was as high as 97.7%. Furthermore, since abnormality of the small intestines usually appears in a group of consecutive images, it would be overlooked only when every abnormal image in the group was misclassified. Thus, as an abnormality screening tool for physicians, the proposed system is valuable.
Keywords: Automatic image recognition; Suspected blood indicator; capsule endoscope; chyme-blockage; multistage recognition; white spots on small intestines

A Multi-task Framework for Skin Lesion Detection and Segmentation - Vesal, Sulaiman; Patil, Shreyas Malakarjun; Ravikumar, Nishant; Maier, Andreas (OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis, 2018)
Early detection and segmentation of skin lesions is crucial for timely diagnosis and treatment, necessary to improve the survival rate of patients. However, manual delineation is time consuming and subject to intra- and inter-observer variations among dermatologists. This underlines the need for an accurate and automatic approach to skin lesion segmentation. To tackle this issue, we propose a multi-task convolutional neural network (CNN) based, joint detection and segmentation framework, designed to initially localize the lesion and subsequently, segment it. A ‘Faster region-based convolutional neural network’ (Faster-RCNN) which comprises a region proposal network (RPN), is used to generate bounding boxes/region proposals, for lesion localization in each image. The proposed regions are subsequently refined using a softmax classifier and a bounding-box regressor. The refined bounding boxes are finally cropped and segmented using ‘SkinNet’, a modified version of U-Net. We trained and evaluated the performance of our network, using the ISBI 2017 challenge and the PH2 datasets, and compared it with the state-of-the-art, using the official test data released as part of the challenge for the former. Our approach outperformed others in terms of Dice coefficients (>0.93), Jaccard index (>0.88), accuracy (>0.96) and sensitivity (>0.95), across five-fold cross validation experiments.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.amulti_3

A Multitask Learning Approach to Assess the Dysarthria Severity in Patients with Parkinson's Disease - Vasquez Correa, Juan; Arias Vergara, Tomás; Orozco-Arroyave, J. R.; Nöth, Elmar (Proceedings of INTERSPEECH, 2018)

An Adaptable Inertial Sensor Fusion-Based Approach for Energy Expenditure Estimation. - Schuldhaus, Dominik; Dorn, Sabrina; Leutheuser, Heike; Tallner, Alexander; Klucken, Jochen; Eskofier, Björn (The 15th International Conference on Biomedical Engineering, 2013)

An algorithm for Parkinson's disease speech classification based on isolated words analysis - Orozco Arroyave, Juan Rafael; Amato, Federica; Borzi, Luigi; Olmo, Gabriella (Health Information Science and Systems, 2021)
Introduction Automatic assessment of speech impairment is a cutting edge topic in Parkinson's disease (PD). Language disorders are known to occur several years earlier than typical motor symptoms, thus speech analysis may contribute to the early diagnosis of the disease. Moreover, the remote monitoring of dysphonia could allow achieving an effective follow-up of PD clinical condition, possibly performed in the home environment. Methods In this work, we performed a multi-level analysis, progressively combining features extracted from the entire signal, the voiced segments, and the on-set/off-set regions, leading to a total number of 126 features. Furthermore, we compared the performance of early and late feature fusion schemes, aiming to identify the best model configuration and taking advantage of having 25 isolated words pronounced by each subject. We employed data from the PC-GITA database (50 healthy controls and 50 PD patients) for validation and testing. Results We implemented an optimized k-Nearest Neighbours model for the binary classification of PD patients versus healthy controls. We achieved an accuracy of 99.4% in 10-fold cross-validation and 94.3% in testing on the PC-GITA database (average value of male and female subjects). Conclusion The promising performance yielded by our model confirms the feasibility of automatic assessment of PD using voice recordings. Moreover, a post-hoc analysis of the most relevant features discloses the option of voice processing using a simple smartphone application.
CRIS-Team WoS Importer:2021-08-13

Analog Non-Linear Transformation-Based Tone Mapping for Image Enhancement in C-arm CT - Shi, Lan; Berger, Martin; Bier, Bastian; Söll, Christopher; Röber, Jürgen; Fahrig, Rebecca; Eskofier, Björn; Maier, Andreas; Maier, Jennifer (IEEE Medical Imaging Conference (MIC), 2016)
Flat-Panel C-arm Computed Tomography (CT) suffers from pixel saturation due to the detector’s limited dynamic range. We describe a novel approach to analog, non-linear tone mapping (TM) for preventing detector saturation. An analog TM operator (TMO) applies a non-linear transformation in a CMOS sensor and its inverse TMO based on 14-bit digital raw data. This is done in order to prevent overexposure and to enhance image quality to 32 bits. The method was applied to the cases of low-contrast head imaging and to that of imaging both knees. Cone-beam projection data with and without overexposure was simulated for a 200° short-scan of the knees and a 360° full-scan of a Forbild head phantom. The results show an increased correlation coefficient of 0.99 compared to 0.96 for overexposed knee data and a higher low-contrast visibility (CC=0.99) compared to linear quantization (CC=0.97
elib2cris::1512001600,shi2016a
Keywords: GRK-1773

Analyse verbreiteter Anwendungen zum Lesen von elektronischen Büchern - Benenson, Zinaida; Berger, Frederik; Cherepantsev, Anatoliy; Datsevich, Sergey; Do, Long; Eckert, Moritz; Elsberger, Tassilo; Freiling, Felix; Frinken, Marius; Glameyer, Hendrik; Hafner, Steffen; Hagenhoff, Svenja; et al. (2019)
Der Marktanteil elektronischer Bucher (E-Books) am Buchmarkt wächst beständig. Um E-Books zu rezipieren, benötigt man spezielle Leseumgebungen, die als Software (im Browser oder als eigene Anwendung) oder als Spezialgerät (E-Reader) realisiert sein können. Diese Leseumgebungen sind geeignet, Daten über das Leseverhalten zu sammeln. Im Rahmen einer universitären Lehrveranstaltung wurden die Software-Leseumgebungen der beiden deutschen Marktführer Kindle und Tolino untersucht. Der vorliegende Bericht fasst die Ergebnisse dieser Analysen zusammen. Das Ergebnis ist eine umfassende Bestandsaufnahme der digitalen Spuren, die durch die Benutzung der Programme entstehen. Betrachtet wurden die zum Untersuchungszeitpunkt aktuellen Versionen der jeweiligen Webanwendungen und Android-Apps sowie des Kindle-Windows-Clients. Die Ergebnisse entstanden im Rahmen einer Übung zur Vorlesung "Fortgeschrittene forensische Informatik II" im Wintersemester 2018/19 an der Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), die gemeinsam durchgeführt wurde vom Lehrstuhl fur Informatik 1 und dem Institut für Buchwissenschaft an der FAU.

Analysis and evaluation of handwriting in patients with Parkinson’s disease using kinematic, geometrical, and non-linear features - Rios-Urrego, Cristian David; Vargas-Bonilla, Francisco; Nöth, Elmar; Lopera, Francisco; Orozco Arroyave, Juan Rafael (Computer Methods and Programs in Biomedicine, 2019)

Analysis and visualization of sleep stages based on deep neural networks - Krauß, Patrick; Metzner, Claus; Joshi, Nidhi; Schulze, Holger; Traxdorf, Maximilian; Maier, Andreas; Schilling, Achim (Neurobiology of Sleep and Circadian Rhythms, 2021)
Automatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages would save human resources and simplify clinical routines. Due to novel open-source software libraries for machine learning, in combination with enormous recent progress in hardware development, a paradigm shift in the field of sleep research towards automatic diagnostics might be imminent. We argue that modern machine learning techniques are not just a tool to perform automatic sleep stage classification, but are also a creative approach to find hidden properties of sleep physiology. We have already developed and established algorithms to visualize and cluster EEG data, facilitating first assessments on sleep health in terms of sleep-apnea and consequently reduced daytime vigilance. In the following study, we further analyze cortical activity during sleep by determining the probabilities of momentary sleep stages, represented as hypnodensity graphs and then computing vectorial cross-correlations of different EEG channels. We can show that this measure serves to estimate the period length of sleep cycles and thus can help to find disturbances due to pathological conditions.
CRIS-Team Scopus Importer:2021-03-19
Keywords: Artificial neural networks; Deep learning; Electroencephalography (EEG); Hypnodensity graphs; Multidimensional scaling (MDS); Polysomnography (PSG); Sleep cycle analysis; Sleep stage scoring

Analysis by adversarial synthesis - A novel approach for speech vocoding - Mustafa, Ahmed; Biswas, Arijit; Bergler, Christian; Schottenhamml, Julia; Maier, Andreas (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019)
Classical parametric speech coding techniques provide a compact representation for speech signals. This affords a very low transmission rate but with a reduced perceptual quality of the reconstructed signals. Recently, autoregressive deep generative models such as WaveNet and SampleRNN have been used as speech vocoders to scale up the perceptual quality of the reconstructed signals without increasing the coding rate. However, such models suffer from a very slow signal generation mechanism due to their sample-by-sample modelling approach. In this work, we introduce a new methodology for neural speech vocoding based on generative adversarial networks (GANs). A fake speech signal is generated from a very compressed representation of the glottal excitation using conditional GANs as a deep generative model. This fake speech is then refined using the LPC parameters of the original speech signal to obtain a natural reconstruction. The reconstructed speech waveforms based on this approach show a higher perceptual quality than the classical vocoder counterparts according to subjective and objective evaluation scores for a dataset of 30 male and female speakers. Moreover, the usage of GANs enables to generate signals in one-shot compared to autoregressive generative models. This makes GANs promising for exploration to implement high-quality neural vocoders.
CRIS-Team Scopus Importer:2019-11-19
Keywords: Deep learning; Generative adversarial networks; Neural vocoder; Speech coding

Analysis of bias induced by various forward projection models in iterative reconstruction - Schmitt, Katharina; Schöndube, Harald; Stierstorfer, Karl; Hornegger, Joachim; Noo, Frédéric (Proceedings of the second international conference on image formation in x-ray computed tomography, 2012)

Analysis of electrophysiological and mechanical dimensions of swallowing by non-invasive biosignals - Roldan-Vasco, Sebastian; Restrepo-Uribe, Juan Pablo; Orozco-Duque, Andres; Suarez-Escudero, Juan Camilo; Orozco Arroyave, Juan Rafael (Biomedical Signal Processing and Control, 2023)
Objective: Alterations in the neuromuscular coordination of swallowing are known as dysphagia, which can produce malnutrition, dehydration and aspiration pneumonia. Its instrumental diagnosis is invasive and expertise dependent. Thus, we introduced a non-invasive multimodal approach for dysphagia screening using surface electromyography (sEMG) and accelerometry-based cervical auscultation (Acc). Methods: Thirty healthy individuals and 30 patients with functional oropharyngeal dysphagia were recruited. Swallowing tasks of saliva and 5, 10, and 20 mL of yogurt and water were performed. Supra- and infrahyoid sEMG and tri-axial Acc signals were recorded. Linear and non-linear features were extracted and selected. Two unimodal and one multimodal classification scenarios were tested. Classical algorithms were applied and the Area Under the ROC curve (AUC) was the criterion for hyperparameters optimization. Results: The Acc related features were the most consistently selected. Although the classification results with Acc signals were higher than with sEMG, the signal fusion improved the unimodal results regardless of swallowing task (AUC > 0.82). The highest classification results were achieved with small volumes of water (AUC = 0.86 ± 0.15) and yogurt (AUC = 0.87 ± 0.12). Conclusion: The combination of non-invasive sEMG and Acc signals improves the performance of automatic classification models for dysphagia detection. Significance: This paper proposes a multimodal approach based on electrophysiological and mechanical swallowing dimensions, for automatic, non-invasive and quantitative dysphagia screening.
CRIS-Team Scopus Importer:2023-01-06
Keywords: Accelerometry; Dysphagia; Machine learning; Multiple signal classification; Surface electromyography (EMG); Swallowing

Analysis of Forming Limits in Sheet Metal Forming with Pattern Recognition Methods. Part 1: Characterization of Onset of Necking and Expert Evaluation - Affronti, Emanuela; Jaremenko, Christian; Merklein, Marion; Maier, Andreas (Materials, 2018)
In automotive manufacturing, high strength materials, and aluminum alloys are widely used to address the requirement of ensuring a lightweight car body and correspondingly, reducing pollution. In this context of complexity of materials and structures, an optimized process design with finite element analyses (FEA) is mandatory, as well as a correct definition of the material forming limits. For this purpose, in sheet metal forming, the forming limit curve (FLC) is used. The FLC is defined by the onset of necking. The standard evaluation method according to DIN EN ISO 12004-2 is based on the cross-section method and assumes that the failure occurs due to a clear localized necking. However, this approach has its limitations, specifically in the case of brittle materials that do not exhibit a distinct necking phase. To overcome this challenge, a pattern recognition-based evaluation is proposed. Although pattern recognition and machine learning techniques have been widely employed in the medical field, few studies have investigated them in the context of analyzing metal sheet forming limits. The application of pattern recognition in metal forming is subject to the exact definition of the forming behaviors. Thereby, it is challenging to relate patterns on the strain distribution during Nakajima tests with the onset of necking for the FLC determination. Thus, the first approach was based on the crack evaluation, since this class is well-defined. However, of substantial interest is the evaluation of the general material instabilities that precede failure. Therefore, in the present study, the analysis of the material behavior during stretching is conducted in order to characterize instability classes. The results of Nakajima tests are investigated using an optical measurement system. A conventional pattern recognition approach based on texture features, considering the outcomes of expert interviews for the definition of classes is used for the FLC determination. Moreover, an analysis of the validity of the supervised learning is conducted. The results show a good prediction of the onset of necking, even for high strength materials with a recall of up to 92%. Some deviations are observed in the determination of the diffuse necking. The discrepancies of the different experts' prognoses highlight the user-dependency of the FLC, suggesting further investigations with an data-driven approach, could be beneficial.
LFT Import::2019-07-29 (2319)
Keywords: Forming limit curve; Machine learning; Pattern recognition; Sheet metal forming

Analysis of Forming Limits in Sheet Metal Forming with Pattern Recognition Methods. Part 2: Unsupervised Methodology and Application - Jaremenko, Christian; Affronti, Emanuela; Maier, Andreas; Merklein, Marion (Materials, 2018)
The forming limit curve (FLC) is used in finite element analysis (FEA) for the modeling of onset of sheet metal instability during forming. The FLC is usually evaluated by achieving forming measurements with optical measurement system during Nakajima tests. Current evaluation methods such as the standard method according to DIN EN ISO 12004-2 and time-dependent methods limit the evaluation range to a fraction of the available information and show weaknesses in the context of brittle materials that do not have a pronounced constriction phase. In order to meet these challenges, a supervised pattern recognition method was proposed, whose results depend on the quality of the expert annotations. In order to alleviate this dependence on experts, this study proposes an unsupervised classification approach that does not require expert annotations and allows a probabilistic evaluation of the onset of localized necking. For this purpose, the results of the Nakajima tests are examined with an optical measuring system and evaluated using an unsupervised classification method. In order to assess the quality of the results, a comparison is made with the time-dependent method proposed by Volk and Hora, as well as expert annotations, while validated with metallographic investigations. Two evaluation methods are presented, the deterministic FLC, which provides a lower and upper limit for the onset of necking, and a probabilistic FLC, which allows definition of failure quantiles. Both methods provide a necking range that shows good correlation with the expert opinion as well as the results of the time-dependent method and metallographic examinations.
LFT Import::2019-07-29 (2327)
Keywords: Forming limit curve; Machine learning; Pattern recognition; Sheet metal forming

ANALYSIS OF HYPERNASAL SPEECH IN CHILDREN WITH CLEFT LIP AND PALATE - Maier, Andreas; Reuss, Alexander; Hacker, Christian; Schuster, Maria; Nöth, Elmar (Text, Speech and Dialogue, 2008)

Analysis of Pathological Speech Signals - Arias Vergara, Tomás (Logos Verlag Berlin GmbH, 2022)
This book addresses the automatic analysis of speech disorders resulting from a clinical condition (Parkinson's disease and hearing loss) or the natural aging process. For Parkinson's disease, the progression of speech symptoms is evaluated by considering speech recordings captured in the short-term (4 months) and long-term (5 years). Machine learning methods are used to perform three tasks: (1) automatic classification of patients vs. healthy speakers. (2) regression analysis to predict the dysarthria level and neurological state. (3) speaker embeddings to analyze the progression of the speech symptoms over time. For hearing loss, automatic acoustic analysis is performed to evaluate whether the duration and onset of deafness (before or after speech acquisition) influence the speech production of cochlear implant users. Additionally, articulation, prosody, and phonemic analyses show that cochlear implant users present altered speech production even after hearing rehabilitatio
Keywords: Speech processing; Machine learning; Deep Learning; Speech disorders; Aging; Smartphone applications; Acoustic Features; Pathological speech; Parkinson's disease; Cochlear Implant Users; Hearing loss

Analysis of Speech from People with Parkinson's Disease through Nonlinear Dynamics - Orozco-Arroyave, Juan Rafael; Arias-Londoño, Julián David; Vargas-Bonilla, Jesús Francisco; Nöth, Elmar (Springer, 2013)
Different characterization approaches, including nonlinear dynamics (NLD), have been addressed for the automatic detection of PD; however, the obtained discrimination capability when only NLD features are considered has not been evaluated yet. This paper evaluates the discrimination capability of a set with ten different NLD features in the task of automatic classification of speech signals from people with Parkinson's disease (PPD) and a control set (CS). The experiments presented in this paper are performed considering the five Spanish vowels uttered by 20 PPD and 20 people from the CS. According the results, it is possible to achieve accuracy rates of up to 76,81% considering only utterances from the vowel/i/. When features calculated from the five Spanish vowels are combined, the performance of the system is not improved, indicating that the inclusion of more NLD features to the system does not guarantee better performance. © 2013 Springer-Verlag Berlin Heidelberg.
UnivIS-Import:2017-12-18:Pub.2013.tech.IMMD.IMMD5.analys
Keywords: complexity measures; Nonlinear dynamics; Parkinson's disease; speech signals

Analysis of Vertical and Horizontal Circular C-Arm Trajectories - Maier, Andreas; Choi, Jang-Hwan; Keil, Andreas; Niebler, Christine; Sarmiento, Marily; Fieselmann, Andreas; Gold, Garry; Delp, Scott; Fahrig, Rebecca (Proc. SPIE Vol. 7961, 2011)

Analysis of visual appearance of retinal nerve fibers in high resolution fundus images: a study on normal subjects - Kolar, Radim; Tornow, Ralf-Peter; Lämmer, Robert; Odstrcilik, Jan; Mayer, Markus Anton; Gazarek, Jiri; Jan, Jiri; Kubena, Tomas; Cernosek, Pavel (Computational and Mathematical Methods in Medicine, 2013)
The retinal ganglion axons are an important part of the visual system, which can be directly observed by fundus camera. The layer they form together inside the retina is the retinal nerve fiber layer (RNFL). This paper describes results of a texture RNFL analysis in color fundus photographs and compares these results with quantitative measurement of RNFL thickness obtained from optical coherence tomography on normal subjects. It is shown that local mean value, standard deviation, and Shannon entropy extracted from the green and blue channel of fundus images are correlated with corresponding RNFL thickness. The linear correlation coefficients achieved values 0.694, 0.547, and 0.512 for respective features measured on 439 retinal positions in the peripapillary area from 23 eyes of 15 different normal subjects.
EVALuna2:21171

Analysis of voice-onset using active rays and hidden markov models - Jesorsky, Oliver; Denzler, Joachim; Nöth, Elmar; Wittenberg, Thomas (Advances in Quantitative Laryngoscopy, 1997)

Analytical Solution for the Optimal Addition of an Item to a Composite of Scores for Maximum Reliability - Ferrer, Carlos A.; Torres-Rodríguez, Idileisy; Taboada-Crispi, Alberto; Nöth, Elmar (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
This paper presents a derivation of the optimal weight to be assigned for an item so that it maximally increases the reliability of the aggregate. This aggregate is the best estimate of the underlying true repeating pattern. The approach differs from previous solutions in being analytical, based on the Signal to Noise Ratio (SNR) instead of the reliability itself, and the ability to visually inform the researcher about the relevance of the weighting strategy and the gains produced in the SNR. Optimal weighting of repetitive phenomena is a bonus not only in the behavioral sciences, but also in many engineering fields. Its uses may include the selection or discarding of raters, judges, repetitions, or epochs, depending on the field.
CRIS-Team Scopus Importer:2019-12-10
Keywords: Composites; Ensemble Averages; Reliability; Signal-to-Noise Ratio

Analytic long-term forecasting with periodic Gaussian processes - Haji Ghassemi, Nooshin; Deisenroth, Marc (Proc. of AISTATS, 2014)

Analytic noise propagation for anisotropic denoising of ct images - Borsdorf, Anja; Kappler, Steffen; Raupach, Rainer; Hornegger, Joachim (2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008, 2008)
In X-ray Computed Tomography (CT) the measured projections and consequently the reconstructed CT images are subject to quantum and electronics noise. While noise in the projections can be well described and estimated with a corresponding physics model, the distribution of noise in the reconstructed CT images is not directly evident. Due to attenuation variations along different directions, the nature of noise in CT images is nonstationary and directed. This complicates the direct application of standard post-processing methods like bilateral filtering. this article we describe a possibUity to compute precise orientation dePendent noise estimates for every pixel position. This is done by analytic propagation of projection noise estimates through indirect fan-beam filtered backprojection reconstruction. The resulting orientation dePendent image noise estimates are subsequently used in adaptive bUateral filters. Taking into account the non-stationary and non-isotropic nature of noise in CT images, an averag improvement in SNR of about 60% is achieved compared to linear filtering at the same resolution. ©2008 IEEE.

Analytic noise-propagation in indirect fan-beam FBP reconstruction - Borsdorf, Anja; Kappler, Steffen; Raupach, Rainer; Hornegger, Joachim (Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, 2008)
Precise knowledge of the local image noise is an essential ingredient to efficient application of post-processing methods such as wavelet or diffusion filtering to computed tomography (CT) images. The non-stationary, object dependent nature of noise in CT images is a direct result from the noise present in the projection data. Since quantum and electronics noise are the dominating noise sources, comparably simple models can be used for direct noise estimates in the individual projections. In this article, we describe the analytic propagation of these noise estimates through fan-beam filtered backprojection (FBP) reconstruction. Contrary to earlier publications in this field, we include the correlations within the parallel projections resulting from the rebinning, the convolution, and the backprojection processes. The method has been validated against Monte-Carlo results and good accuracy with an average relative error below 3.6% was achieved for arbitrary objects and over the full range of commonly used convolution kernels and field-of-view settings.

Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach - Bayer, Siming; Zhong, Xia; Fu, Weilin; Ravikumar, Nishant; Maier, Andreas (Algorithmen – Systeme – Anwendungen. Proceedings des Workshops, 2020)
Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions. The differences between intra-patient images can be assessed quantitatively by registering serial acquisitions. Due to the variability of the images (i.e. contrast, luminosity) and the anatomical changes of the retina, the registration of fundus images remains a challenging task. Recently, several deep learning approaches have been proposed to register fundus images in an end-to-end fashion, achieving remarkable results.However, the results are difficult to interpret and analyze. In this work, we propose an imitation learning framework for the registration of 2D color funduscopic images for a wide range of applications such as disease monitoring, image stitching and super-resolution. We follow a divide-and-conquer approach to improve the interpretability of the proposed network, and analyze both the influence of the input image and the hyperparameters on the registration result.The results show that the proposed registration network reduces the initial target registration error up to 95 %<br

An analysis of edge detection as a feature extractor in a hand gesture recognition system based on nearest neighbor - Lionnie, Regina; Timotius, Ivanna; Setyawan, Iwan (2011 International Conference on Electrical Engineering and Informatics (ICEEI), 2011)
Hand gestures, as a part of human body language, can be used for many purposes. By means of a hand gesture recognizer, we could communicate with machines using our hand gestures. A recognition system typically consists of pre-processing steps and a classifier. This paper presents an analysis of using edge detection and/or histogram equalization in the pre-processor by examining the overall performance of the hand gesture recognition system. Nearest neighbor classifier is used as a classifier in the recognition system. The system aims to classify the input images into one of six classes. Each class represents a different command to a machine. The hand gesture images are taken using a web camera under controlled condition and a uniform white background. The system performance is measured by using cross validation method. The experiment results show that using histogram equalization and edge detection as feature extractor lowered the average of accuracy.
Keywords: hand gestures recognition; nearest neighbor; edge detection; histogram equalization.

An Analysis of Perplexity to Reveal the Effects of Alzheimer's Disease on Language - Wankerl, Sebastian; Nöth, Elmar; Evert, Stephanie (ITG-Fachbericht 267: Speech Communication, 2016)

An anthropomorphic deformable phantom for brain shift simulation - Bayer, Siming; Wydra, Adrian; Zhong, Xia; Ravikumar, Nishant; Strumia, Maddalena; Schaffert, Roman; Ostermeier, Martin; Fahrig, Rebecca; Maier, Andreas (2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2018)
The prominent soft tissue deformation during a neurosurgical procedure, the so called brain shift phenomenon, affects the accuracy of the surgery greatly. Although the feasibility of numerous intraoperative modalities is investigated for the brain shift compensation, another state-of-the-art interventional imaging modality – C-Arm CT is rarely studied in this context. Due to the lack of clinical data, a suitable anthropomorphic deformable phantom is indispensable. The purpose of this study is to describe and determine the characteristics of a multi-modal deformable brain phantom made out of polyurethane and ceramic composite bone material. The phantom is made of six parts: skin, skull, brain, blood vessels, ventricles and an inflatable tumor. Fluid can be filled into the blood vessels, the ventricles and the tumor in order to simulate the brain shift phenomenon. Such an anthropomorphic phantom is both visible with MR and C-Arm CT scanner. In order to evaluate the clinical relevance of our phantom, we induce deformations with different degrees by in- and deflated the embedded tumor in 5 steps. Both MR and C-Arm CT (cone beam CT) images are acquired in each step. The induced deformation is estimated by performing mono-modal deformable registration with symmetric normalization method (SyN) both on MR and cone beam CT (CBCT) acquisitions. The maximum displacement estimated with MR and CBCT pair is 29.6mm and 28.1mm. This result correlates the clinical findings.<br
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.ananth_9

An approximation of the Gaussian RBF kernel for efficient classification with SVMs - Ring, Matthias; Eskofier, Björn (Pattern Recognition Letters, 2016)
In theory, kernel support vector machines (SVMs) can be reformulated to linear SVMs. This reformulation can speed up SVM classifications considerably, in particular, if the number of support vectors is high. For the widely-used Gaussian radial basis function (RBF) kernel, however, this theoretical fact is impracticable because the reproducing kernel Hilbert space (RKHS) of this kernel has infinite dimensionality. Therefore, we derive a finite-dimensional approximative feature map, based on an orthonormal basis of the kernel’s RKHS, to enable the reformulation of Gaussian RBF SVMs to linear SVMs. We show that the error of this approximative feature map decreases with factorial growth if the approximation quality is linearly increased. Experimental evaluations demonstrated that the approximative feature map achieves considerable speed-ups (about 18-fold on average), mostly without losing classification accuracy. Therefore, the proposed approximative feature map provides an efficient SVM evaluation method with minimal loss of precision.

Anatomical accuracy of abdominal lesion localization: Retrospective automatic rigid image registration between FDG-PET and MRI - Kiefer, Alexander; Kuwert, Torsten; Hahn, Dieter; Hornegger, Joachim; Uder, Michael; Ritt, Philipp (Nuklearmedizin-Nuclear Medicine, 2011)
Software-based image registration can improve the diagnostic value of imaging procedures and is an alternative to hybrid scanners. The aim of this study was to evaluate the anatomical accuracy of automatic rigid image registration of independently acquired datasets of positron emission tomography with F-deoxyglucose and abdominal magnetic resonance imaging. Patients, methods: Analyses were performed on 28 abdominal lesions from 20 patients. The PET data were obtained using a stand-alone PET camera in 14 cases and a hybrid PET/CT scanner in 9 cases. The abdominal T1- and T2-weighted MRI scans were acquired on 1.5 T MRI scanners. The mean time interval between MRI and PET was 7.3 days (0-28 days). Automatic rigid registration was carried out using a self-developed registration tool integrated into commercial available software (InSpace for Siemens Syngo). Distances between the centres of gravity of 28 manually delineated neoplastic lesions represented in PET and MRI were measured in X-, Y-, and Z-direction. The intra- (intraclass correlation 0.94) and inter- (intraclass correlation 0.86) Positronenemissionsobserver repeatability were high. Results: The average distance in all MRI sequences was 5.2 ± 7.6 mm in X-direction, 4. ± 3.7 mm in Y-direction and 6.1 ± 5.1 mm in Z-direction. There was a significantly higher misalignment in Z-direction (p < 0.05). The misalignment was not significantly different for the registration of T1- and T2- weighted sequences (p = 0.7). Conclusion: The misalignment between FDG-PET and abdominal MRI registered using an automated rigid registration tool was comparable to data reported for software-based fusion between PET and CT. Although this imprecision may not affect diagnostic accuracy, it is not sufficient to allow for pixel-wise integration of MRI and PET information. © Schattauer 2011.

Anatomical accuracy of interactive and automated rigid registration between X-ray CT and FDG-PET - Wolz, Gabriele; Nömayr, Anton; Hothorn, Torsten; Hornegger, Joachim; Römer, Wolfgang; Bautz, Werner; Kuwert, Torsten (Nuklearmedizin-Nuclear Medicine, 2007)
Aim: Comparison of anatomical accuracy of software-based interactive (IRR) and automated rigid registration (ARR) of separately acquired CT and FDG-PET data sets. Patients, methods: Independently acquired PET and helical CT data from 22 tumour patients were registered manually using the Syngo advanced Fusion VC20H tool. IRR was performed separately for the thorax and the abdomen using physiological FDG uptake in several organs as a reference. In addition, ARR was performed with the commercially available software tool Mirada 7D on all of the patients. For both methods, the distances between the representation of 53 malignant lesions on PET and CT were measured in X-, Y-, and Z-direction with reference to a common coordinate system (X-, Y-, Z-distances). Results: The percentage of lesions misregistered by less than 1.5 cm was in X-direction 91% for IRR and 89% for ARR; in Y-direction 85% and 68%; in Z-direction 72% and 51%, respectively. The average X-, Y- and Z-distances for IRR ranged from 0.58 ± 0.55 cm (X-direction) to 1.17 ± 1.66 cm (Z-direction). For ARR, the average X-, Y- and Z-distances varied between 0.66 ± 0.61 cm (X-direction) and 1.81 ± 1.37 cm (Z-direction). Mixed effects analysis of the absolute X-, Y- and Z-distances revealed a significantly better alignment for IRR compared to ARR in Z-direction (p <0.01). Lesion size and localization either in thorax or abdomen had no significant influence on the accuracy of registration. Conclusion: For the majority of malignant lesions, manual image registration with the possibility to separately align different body segments was more accurate than the automated approach. Current software for ARR does not reach the anatomical accuracy reported for PET/CT hybrid scanners. © 2007 Schattauer GmbH.

Anatomical accuracy of lesion localization - Nömayr, Anton; Römer, Wolfgang; Hothorn, Torsten; Pfahlberg, Annette; Hornegger, Joachim; Bautz, Werner; Kuwert, Torsten (Nuklearmedizin-Nuclear Medicine, 2005)

Anatomical accuracy of lesion localization: Retrospective interactive rigid image registration between18F-FDG-PET and X-ray CT - Nömayr, Anton; Römer, Wolfgang; Hothorn, Torsten; Pfahlberg, Annette; Hornegger, Joachim; Bautz, Werner; Kuwert, Torsten (Nuklearmedizin-Nuclear Medicine, 2005)
The aim of this study was to evaluate the anatomical accuracy and reproducibility of retrospective interactive rigid image registration (RIR) between routinely archived X-ray computer tomography (CT) and positron emission tomography performed with F-deoxyglucose (FDG-PET) in oncological patients. Methods: Two observers registered PET and CT data obtained in 37 patients using a commercially available image fusion tool. RIR was performed separately for the thorax and the abdomen using physiological FDG uptake in several organs as a reference. One observer performed the procedure twice (O1a and O1b), another person once (O2). For 94 malignant lesions, clearly visible in CT and PET, the signed and absolute distances between their representation on PET and CT were measured in X-, Y-, and Z-direction with reference to a coordinate system centered in the CT representation of each lesion (X-, Y-, Z-distances). Results: The mean differences of the signed and absolute distances between O1a, O1b, and O2 did not exceed 3 mm in any dimension. The absolute X-, Y-, and Z-distances ranged between 0.57 ± 0.58 cm for O1a (X-direction) and 1.12 ± 1.28 cm for O2 (Z-direction). When averaging the absolute distances measured by O1a, O1b, and O2, the percentage of lesions misregistered by less than 1.5 cm was 91% for the X-, 88% for the Y-, and 77% for the Z-direction. The larger error of fusion determined for the remaining lesions was caused by non-rigid body transformations due to differences in breathing, arm position, or bowel movements between the two examinations. Mixed effects analysis of the signed and absolute X-, Y-, and Z-distances disclosed a significantly greater mis-alignment in the thorax than in the abdomen as well as axially than transaxially. Conclusion: The anatomical inaccuracy of RIR can be expected to be < 1.5 cm for the majority of neoplastic foci. Errors of alignment are bigger in the thorax and in Z-direction, due to non-rigid body transformations caused, e.g., by breathing. © 2005 Schattauer GmbH.

An automated data processing method dedicated to 3D ultrasonic non destructive testing of composite pieces - Osman, Ahmad; Haßler, Ulf; Kaftandjian, Valerie; Hornegger, Joachim (International Symposium on Ultrasound in the Control of Industrial Processes, UCIP 2012, 2012)
State-of the art Non Destructive Testing using ultrasound is based on evaluation of C-scan images, which is done mainly visually. The development of the new Sampling Phased Array technique SPA by IZFP Fraunhofer provides a fast three-dimensional reconstruction of inner object structures. This new inspection technique is to be complemented with fully or semi-automated evaluation of ultrasonic data, providing maximum support to the operator. We present in this contribution a processing method for SPA ultrasonic data, where the main focus of this paper will be on speckle noise reduction. The evaluation method is applied on carbon fibre composite where it demonstrates robust and successful performance in recognition of defects.

An automatic algorithm measuring the retinal intercapillary area to assess diabetic retinopathy - Schottenhamml, Julia; Moult, Eric M.; Ploner, Stefan; Lee, Byungkun; Lu, Chen D.; Husvogt, Lennart; Waheed, Nadia K.; Duker, Jay S.; Hornegger, Joachim; Fujimoto, James G. (Investigative Ophthalmology & Visual Science, 2016)
Purpose : Several studies demonstrated the utility of optical coherence tomography angiography (OCTA) in evaluating vascular abnormalities in patients with diabetic retinopathy (DR). Thus far, automatic quantitative analysis has focused on measurements of the foveal avascular zone (FAZ) and capillary density. Analysis of the intercapillary areas (ICA) of the perifoveum using manual segmentation has also been demonstrated. The purpose of this study is to develop a fully automated algorithm for quantifying retinal perfusion impairment using ICA displayed on OCTA images. The longer term objective is to develop sensitive and robust metrics for the diagnosis of DR and to quantify DR progression. Methods : A 1050 nm, 400 kHz swept-source (SS)-OCT system was used to perform OCTA imaging of 89 eyes from 51 diabetic patients and 63 eyes from 32 normal subjects. Of the 89 eyes from diabetic patients, 51 eyes had no clinically detected retinopathy, 29 eyes had non-proliferative diabetic retinopathy (NPDR), and 9 eyes had proliferative diabetic retinopathy (PDR). OCTA en face images of the retinal vasculature were generated. The vessel structure in each image was highlighted using a vesselness filter; next, a binary image was obtained through a marching algorithm designed to exploit continuity constraints. Finally, the ICA were determined via connected components and subsequently used to generate false color images highlighting areas of non-perfusion. Results : Representative ICA maps for a normal eye and a NPDR eye are shown in Figure 1. The ICA maps can visualize areas of non-perfusion and enable rapid assessment of the vascular changes that occur in DR. Similar, but less pronounced changes were observed in eyes of diabetics without retinopathy. Conclusions : A fully automatic algorithm for quantifying ICA in OCTA images was developed. Preliminary results suggest ICA may be a useful metric for assessing diabetes and DR. Further work is needed to develop quantitative measures, validate the algorithm, and evaluate robustness in the presence of OCTA image artifact
UnivIS-Import:2018-09-11:Pub.2016.tech.IMMD.IMMD5.anauto
Keywords: oct; octa; oct angiography; diabetic retinopathy; intercapillary area

An automatic histogram-based initializing algorithm for K-means clustering in CT - Tian, Mengqiu; Yang, Qiao; Maier, Andreas; Schasiepen, Ingo; Maass, Nicole; Elter, Matthias (Proceedings des Workshops Bildverarbeitung für die Medizin 2013, 2013)

An Automatic, Intercapillary Area-based Algorithm for Quantifying Diabetes-related Capillary Dropout Using Optical Coherence Tomography Angiography - Schottenhamml, Julia; Moult, Eric M.; Ploner, Stefan; Lee, Byungkun; Novais, Eduardo A.; Cole, Emily D.; Dang, Sabin; Lu, Chen D.; Husvogt, Lennart; Waheed, Nadia K.; Duker, Jay S.; Hornegger, Joachim; et al. (Retina (Philadelphia, Pa.), 2016)
Purpose: To develop a robust, sensitive, and fully automatic algorithm to quantify diabetes-related capillary dropout using optical coherence tomography (OCT) angiography (OCTA). Methods: A 1,050-nm wavelength, 400 kHz A-scan rate swept-source optical coherence tomography prototype was used to perform volumetric optical coherence tomography angiography imaging over 3 mm × 3 mm fields in normal controls (n = 5), patients with diabetes without diabetic retinopathy (DR) (n = 7), patients with nonproliferative diabetic retinopathy (NPDR) (n = 9), and patients with proliferative diabetic retinopathy (PDR) (n = 5); for each patient, one eye was imaged. A fully automatic algorithm to quantify intercapillary areas was developed. Results: Of the 26 evaluated eyes, the segmentation was successful in 22 eyes (85%). The mean values of the 10 and 20 largest intercapillary areas, either including or excluding the foveal avascular zone, showed a consistent trend of increasing size from normal control eyes, to eyes with diabetic retinopathy but without diabetic retinopathy, to nonproliferative diabetic retinopathy eyes, and finally to PDR eyes. Conclusion: Optical coherence tomography angiography-based screening and monitoring of patients with diabetic retinopathy is critically dependent on automated vessel analysis. The algorithm presented was able to automatically extract an intercapillary areabased metric in patients having various stages of diabetic retinopathy. Intercapillary areabased approaches are likely more sensitive to early stage capillary dropout than vascular density-based method
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.anauto_6
Keywords: Capillary dropout; Diabetic retinopathy; Intercapillary area; Optical coherence tomography; Optical coherence tomography angiography

An Automatic Multimodal Approach to Analyze Linguistic and Acoustic Cues on Parkinson's Disease Patients - Escobar-Grisales, Daniel; Arias-Vergara, Tomás; Rios-Urrego, Cristian David; Nöth, Elmar; García, Adolfo M.; Orozco-Arroyave, Juan Rafael (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2023)
Early detection and monitoring of Parkinson's disease are crucial for properly treating and managing the symptoms. Automatic speech and language analysis has emerged as a promising non-invasive method to monitor the patient's state. This study analyzed different speech and language representations for automatic classification between Parkinson's disease patients and healthy controls. First, each modality is analyzed independently. General representations such as Wav2vec or BETO are used together with representations oriented to model disease traits such as phonemic identifiability in speech modality and grammatical units analysis in language modality. The best speech and language representations were combined using a fusion strategy based on Gated Multimodal Units. The best results are achieved with the multimodal approach, outperforming all results obtained with unimodal representations and the traditional fusion strategy.
CRIS-Team Scopus Importer:2023-09-29
Keywords: language analysis; multimodal; Speech analysis

An Automatic Screening Test for Preschool Children: Theory and Data Collection - Bocklet, Tobias; Winterholler, Cordula; Maier, Andreas; Schuster, Maria; Nöth, Elmar (Proceedings of WOCCI 2009, 2009)

An automatic version of a reading disorder test - Maier, Andreas; Hönig, Florian Thomas; Steidl, Stefan; Nöth, Elmar; Horndasch, Stefanie; Sauerhöfer, Elisabeth; Kratz, Oliver; Moll, Gunther (ACM Transactions on Speech and Language Processing, 2011)