TY - THES A1 - Clasen, Malte T1 - Beleuchtung von Landschaften in interaktiver Darstellung N2 - Inhalt dieser Diplomarbeit ist ein Beleuchtungs- und Visualisierungsmodell für Pflanzen im interaktiven Landschaftsrendering. Ziel ist die qualitativ hochwertige Darstellung von einzelnen Individuen nahe des Betrachters, der seine Position in einer ansonsten statischen Szene frei wählen kann. Um dies zu erreichen wird zunächst vorgestellt, wie ausgehend von einfachen 3D-Modellen und Materialien ein physikalisch basiertes Reflexionsmodell parametrisiert werden kann. Grund hierfür ist der oftmals vorhandene umfangreiche Datenbestand, der auf die OpenGL-Materialien oder ähnliche Shader optimiert ist. Anschließend wird gezeigt, wie die Echtzeit-Renderingtechniken Shadow Mapping und Precomputed Radiance Transfer kombiniert werden können, um sowohl exakte hochfrequente direkte Beleuchtung als auch niederfrequentes indirektes Streulicht zu berücksichtigen. Diese Kombination wird an Hand der Pfad-Notation des Monte-Carlo-Path-Tracing eingeführt und begründet. Y1 - 2005 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-9906 ER - TY - THES A1 - Kähler, Ralf T1 - Accelerated Volume Rendering on Structured Adaptive Meshes N2 - Multi-scale phenomena are abundant in many application fields. Representing and numerically simulating such processes is a challenging task since quite different scales have to be resolved, which often requires enormous amounts of storage and computational power. An important strategy in this context is adaptivity, i.e. local adjustment of the spatio-temporal resolution to the details to be resolved. A standard representation therefore are hierarchical, locally refined grids. A specific adaptive approach for solving partial differential equations, usually called AMR (Adaptive Mesh Refinement), was introduced in 1984. The basic idea is to combine the simplicity of structured grids and the advantages of local refinement. In this numerical scheme the computations are started on a set of coarse, potentially overlapping structured grids, that cover the computational domain. Local error criteria are applied to detect regions that require higher resolution. These are covered by subgrids with decreasing mesh spacing, which do not replace, but rather overlap the refined regions of the coarser patches. The equations are advanced on the finer subgrids and the refinement procedure recursively continues until all cells fulfill the considered error criteria, giving rise to a hierarchy of nested levels of refinement. In 1989 a variant of this scheme, called Structured Adaptive Mesh Refinement (SAMR), which reduces some of the complexity of the original approach, was proposed. While the separate subgrids in the AMR scheme could be rotated against each other, in SAMR they are aligned with the major axes of the coordinate system, which for example simplifies the computation of fluxes of (conserved) quantities through the cell faces. SAMR has become more and more popular in the last decade, and nowadays it is applied in many domains like hydrodynamics, meteorology and in particular in cosmology and relativistic astrophysics. Due to this growing popularity, an increasing number of scientists is in need of appropriate interactive visualization techniques to interpret and analyze AMR simulation data. Tools for both, 2D analysis to quantitatively convey the information within single slices and 3D representations to apprehend the overall structure are required. In this thesis we develop direct and indirect volume visualization algorithms for scalar fields that are defined on structured Adaptive Mesh Refinement (SAMR) grids. In particular algorithms for planar slicing and the display of height fields, C0-continuous isosurface extraction, software-, and hardware-based direct volume rendering and temporal interpolation for cell-, and vertex-centered data on unrestricted SAMR grids are proposed. Additionally we investigate the applicability of SAMR data structures for accelerated software-, and hardware-based volume rendering of large 3D scalar data. KW - Visualization KW - Volume Rendering KW - Adaptive Mesh Refinement Data Y1 - 2005 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:188-2005002769 ER - TY - GEN A1 - Grewe, C. Martin A1 - Zachow, Stefan T1 - Release of the FexMM for the Open Virtual Mirror Framework N2 - THIS MODEL IS FOR NON-COMMERCIAL RESEARCH PURPOSES. ONLY MEMBERS OF UNIVERSITIES OR NON-COMMERCIAL RESEARCH INSTITUTES ARE ELIGIBLE TO APPLY. 1. Download, fill, and sign the form available from: https://media.githubusercontent.com/media/mgrewe/ovmf/main/data/fexmm_license_agreement.pdf 2. Send the signed form to: fexmm@zib.de NOTE: Use an official email address of your institution for the request. Y1 - 2021 U6 - https://doi.org/10.12752/8532 ER - TY - JOUR A1 - von Lindheim, Johannes A1 - Harikrishnan, Abhishek A1 - Dörffel, Tom A1 - Klein, Rupert A1 - Koltai, Peter A1 - Mikula, Natalia A1 - Müller, Annette A1 - Névir, Peter A1 - Pacey, George A1 - Polzin, Robert A1 - Vercauteren, Nikki T1 - Definition, detection and tracking of persistent structures in atmospheric flows JF - arXiv N2 - Long-lived flow patterns in the atmosphere such as weather fronts, mid-latitude blockings or tropical cyclones often induce extreme weather conditions. As a consequence, their description, detection, and tracking has received increasing attention in recent years. Similar objectives also arise in diverse fields such as turbulence and combustion research, image analysis, and medical diagnostics under the headlines of "feature tracking", "coherent structure detection" or "image registration" - to name just a few. A host of different approaches to addressing the underlying, often very similar, tasks have been developed and successfully used. Here, several typical examples of such approaches are summarized, further developed and applied to meteorological data sets. Common abstract operational steps form the basis for a unifying framework for the specification of "persistent structures" involving the definition of the physical state of a system, the features of interest, and means of measuring their persistence. Y1 - 2021 ER - TY - JOUR A1 - Tack, Alexander A1 - Ambellan, Felix A1 - Zachow, Stefan T1 - Towards novel osteoarthritis biomarkers: Multi-criteria evaluation of 46,996 segmented knee MRI data from the Osteoarthritis Initiative JF - PLOS One N2 - Convolutional neural networks (CNNs) are the state-of-the-art for automated assessment of knee osteoarthritis (KOA) from medical image data. However, these methods lack interpretability, mainly focus on image texture, and cannot completely grasp the analyzed anatomies’ shapes. In this study we assess the informative value of quantitative features derived from segmentations in order to assess their potential as an alternative or extension to CNN-based approaches regarding multiple aspects of KOA. Six anatomical structures around the knee (femoral and tibial bones, femoral and tibial cartilages, and both menisci) are segmented in 46,996 MRI scans. Based on these segmentations, quantitative features are computed, i.e., measurements such as cartilage volume, meniscal extrusion and tibial coverage, as well as geometric features based on a statistical shape encoding of the anatomies. The feature quality is assessed by investigating their association to the Kellgren-Lawrence grade (KLG), joint space narrowing (JSN), incident KOA, and total knee replacement (TKR). Using gold standard labels from the Osteoarthritis Initiative database the balanced accuracy (BA), the area under the Receiver Operating Characteristic curve (AUC), and weighted kappa statistics are evaluated. Features based on shape encodings of femur, tibia, and menisci plus the performed measurements showed most potential as KOA biomarkers. Differentiation between non-arthritic and severely arthritic knees yielded BAs of up to 99%, 84% were achieved for diagnosis of early KOA. Weighted kappa values of 0.73, 0.72, and 0.78 were achieved for classification of the grade of medial JSN, lateral JSN, and KLG, respectively. The AUC was 0.61 and 0.76 for prediction of incident KOA and TKR within one year, respectively. Quantitative features from automated segmentations provide novel biomarkers for KLG and JSN classification and show potential for incident KOA and TKR prediction. The validity of these features should be further evaluated, especially as extensions of CNN- based approaches. To foster such developments we make all segmentations publicly available together with this publication. Y1 - 2021 U6 - https://doi.org/10.1371/journal.pone.0258855 VL - 16 IS - 10 ER - TY - JOUR A1 - Tack, Alexander A1 - Shestakov, Alexey A1 - Lüdke, David A1 - Zachow, Stefan T1 - A deep multi-task learning method for detection of meniscal tears in MRI data from the Osteoarthritis Initiative database JF - Frontiers in Bioengineering and Biotechnology, section Biomechanics N2 - We present a novel and computationally efficient method for the detection of meniscal tears in Magnetic Resonance Imaging (MRI) data. Our method is based on a Convolutional Neural Network (CNN) that operates on a complete 3D MRI scan. Our approach detects the presence of meniscal tears in three anatomical sub-regions (anterior horn, meniscal body, posterior horn) for both the Medial Meniscus (MM) and the Lateral Meniscus (LM) individually. For optimal performance of our method, we investigate how to preprocess the MRI data or how to train the CNN such that only relevant information within a Region of Interest (RoI) of the data volume is taken into account for meniscal tear detection. We propose meniscal tear detection combined with a bounding box regressor in a multi-task deep learning framework to let the CNN implicitly consider the corresponding RoIs of the menisci. We evaluate the accuracy of our CNN-based meniscal tear detection approach on 2,399 Double Echo Steady-State (DESS) MRI scans from the Osteoarthritis Initiative database. In addition, to show that our method is capable of generalizing to other MRI sequences, we also adapt our model to Intermediate-Weighted Turbo Spin-Echo (IW TSE) MRI scans. To judge the quality of our approaches, Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values are evaluated for both MRI sequences. For the detection of tears in DESS MRI, our method reaches AUC values of 0.94, 0.93, 0.93 (anterior horn, body, posterior horn) in MM and 0.96, 0.94, 0.91 in LM. For the detection of tears in IW TSE MRI data, our method yields AUC values of 0.84, 0.88, 0.86 in MM and 0.95, 0.91, 0.90 in LM. In conclusion, the presented method achieves high accuracy for detecting meniscal tears in both DESS and IW TSE MRI data. Furthermore, our method can be easily trained and applied to other MRI sequences. Y1 - 2021 U6 - https://doi.org/10.3389/fbioe.2021.747217 SP - 28 EP - 41 ER - TY - GEN A1 - Tack, Alexander A1 - Shestakov, Alexey A1 - Lüdke, David A1 - Zachow, Stefan T1 - A deep multi-task learning method for detection of meniscal tears in MRI data from the Osteoarthritis Initiative database N2 - We present a novel and computationally efficient method for the detection of meniscal tears in Magnetic Resonance Imaging (MRI) data. Our method is based on a Convolutional Neural Network (CNN) that operates on a complete 3D MRI scan. Our approach detects the presence of meniscal tears in three anatomical sub-regions (anterior horn, meniscal body, posterior horn) for both the Medial Meniscus (MM) and the Lateral Meniscus (LM) individually. For optimal performance of our method, we investigate how to preprocess the MRI data or how to train the CNN such that only relevant information within a Region of Interest (RoI) of the data volume is taken into account for meniscal tear detection. We propose meniscal tear detection combined with a bounding box regressor in a multi-task deep learning framework to let the CNN implicitly consider the corresponding RoIs of the menisci. We evaluate the accuracy of our CNN-based meniscal tear detection approach on 2,399 Double Echo Steady-State (DESS) MRI scans from the Osteoarthritis Initiative database. In addition, to show that our method is capable of generalizing to other MRI sequences, we also adapt our model to Intermediate-Weighted Turbo Spin-Echo (IW TSE) MRI scans. To judge the quality of our approaches, Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values are evaluated for both MRI sequences. For the detection of tears in DESS MRI, our method reaches AUC values of 0.94, 0.93, 0.93 (anterior horn, body, posterior horn) in MM and 0.96, 0.94, 0.91 in LM. For the detection of tears in IW TSE MRI data, our method yields AUC values of 0.84, 0.88, 0.86 in MM and 0.95, 0.91, 0.90 in LM. In conclusion, the presented method achieves high accuracy for detecting meniscal tears in both DESS and IW TSE MRI data. Furthermore, our method can be easily trained and applied to other MRI sequences. T3 - ZIB-Report - 21-33 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-84415 SN - 1438-0064 ER - TY - CHAP A1 - Hanik, Martin A1 - Hege, Hans-Christian A1 - von Tycowicz, Christoph T1 - A Nonlinear Hierarchical Model for Longitudinal Data on Manifolds T2 - 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) N2 - Large longitudinal studies provide lots of valuable information, especially in medical applications. A problem which must be taken care of in order to utilize their full potential is that of correlation between intra-subject measurements taken at different times. For data in Euclidean space this can be done with hierarchical models, that is, models that consider intra-subject and between-subject variability in two different stages. Nevertheless, data from medical studies often takes values in nonlinear manifolds. Here, as a first step, geodesic hierarchical models have been developed that generalize the linear ansatz by assuming that time-induced intra-subject variations occur along a generalized straight line in the manifold. However, this is often not the case (e.g., periodic motion or processes with saturation). We propose a hierarchical model for manifold-valued data that extends this to include trends along higher-order curves, namely Bézier splines in the manifold. To this end, we present a principled way of comparing shape trends in terms of a functional-based Riemannian metric. Remarkably, this metric allows efficient, yet simple computations by virtue of a variational time discretization requiring only the solution of regression problems. We validate our model on longitudinal data from the osteoarthritis initiative, including classification of disease progression. Y1 - 2022 U6 - https://doi.org/10.1109/ISBI52829.2022.9761465 SP - 1 EP - 5 ER - TY - GEN A1 - Ambellan, Felix A1 - Hanik, Martin A1 - von Tycowicz, Christoph T1 - Morphomatics: Geometric morphometrics in non-Euclidean shape spaces N2 - Morphomatics is an open-source Python library for (statistical) shape analysis developed within the geometric data analysis and processing research group at Zuse Institute Berlin. It contains prototype implementations of intrinsic manifold-based methods that are highly consistent and avoid the influence of unwanted effects such as bias due to arbitrary choices of coordinates. KW - shape analysis KW - geometric statistics KW - geometric morphometrics Y1 - 2021 U6 - https://doi.org/10.12752/8544 N1 - https://morphomatics.github.io/ ER - TY - JOUR A1 - Grewe, Carl Martin A1 - Liu, Tuo A1 - Hildebrandt, Andrea A1 - Zachow, Stefan T1 - The Open Virtual Mirror Framework for Enfacement Illusions - Enhancing the Sense of Agency With Avatars That Imitate Facial Expressions JF - Behavior Research Methods Y1 - 2022 U6 - https://doi.org/10.3758/s13428-021-01761-9 PB - Springer ER - TY - JOUR A1 - Jain, Toshit A1 - Singh, Upkar A1 - Singh, Varun A1 - Boda, Vijay Kumar A1 - Hotz, Ingrid A1 - Vadhiyar, Sathish S. A1 - Vinayachandran, P. N. A1 - Natarajan, Vijay T1 - A Scalable System for Visual Analysis of Ocean Data JF - Computer Graphics Forum Y1 - 2025 U6 - https://doi.org/10.1111/cgf.15279 VL - 44 IS - 1 SP - e15279 ER - TY - CHAP A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - Geodesic B-Score for Improved Assessment of Knee Osteoarthritis T2 - Proc. Information Processing in Medical Imaging (IPMI) N2 - Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination ability over its Euclidean counterpart, which we demonstrate for predictive validity on assessing risks of total knee replacement. This result highlights the potential of the geodesic B-score to enable improved personalized assessment and stratification for interventions. Y1 - 2021 U6 - https://doi.org/10.1007/978-3-030-78191-0_14 SP - 177 EP - 188 ER - TY - JOUR A1 - Hembus, Jessica A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - Bader, Rainer T1 - Establishment of a rolling-sliding test bench to analyze abrasive wear propagation of different bearing materials for knee implants JF - Applied Sciences N2 - Currently, new materials for knee implants need to be extensively and expensive tested in a knee wear simulator in a realized design. However, using a rolling-sliding test bench, these materials can be examined under the same test conditions but with simplified geometries. In the present study, the test bench was optimized, and forces were adapted to the physiological contact pressure in the knee joint using the available geometric parameters. Various polymers made of polyethylene and polyurethane articulating against test wheels made of cobalt-chromium and aluminum titanate were tested in the test bench using adapted forces based on ISO 14243-1. Polyurethane materials showed distinctly higher wear rates than polyethylene materials and showed inadequate wear resistance for use as knee implant material. Thus, the rolling-sliding test bench is an adaptable test setup for evaluating newly developed bearing materials for knee implants. It combines the advantages of screening and simulator tests and allows testing of various bearing materials under physiological load and tribological conditions of the human knee joint. The wear behavior of different material compositions and the influence of surface geometry and quality can be initially investigated without the need to produce complex implant prototypes of total knee endoprosthesis or interpositional spacers. Y1 - 2021 U6 - https://doi.org/10.3390/app11041886 VL - 11 IS - 4 ER - TY - GEN A1 - Tack, Alexander A1 - Ambellan, Felix A1 - Zachow, Stefan T1 - Towards novel osteoarthritis biomarkers: Multi-criteria evaluation of 46,996 segmented knee MRI data from the Osteoarthritis Initiative (Supplementary Material) T2 - PLOS One N2 - Convolutional neural networks (CNNs) are the state-of-the-art for automated assessment of knee osteoarthritis (KOA) from medical image data. However, these methods lack interpretability, mainly focus on image texture, and cannot completely grasp the analyzed anatomies’ shapes. In this study we assess the informative value of quantitative features derived from segmentations in order to assess their potential as an alternative or extension to CNN-based approaches regarding multiple aspects of KOA A fully automated method is employed to segment six anatomical structures around the knee (femoral and tibial bones, femoral and tibial cartilages, and both menisci) in 46,996 MRI scans. Based on these segmentations, quantitative features are computed, i.e., measurements such as cartilage volume, meniscal extrusion and tibial coverage, as well as geometric features based on a statistical shape encoding of the anatomies. The feature quality is assessed by investigating their association to the Kellgren-Lawrence grade (KLG), joint space narrowing (JSN), incident KOA, and total knee replacement (TKR). Using gold standard labels from the Osteoarthritis Initiative database the balanced accuracy (BA), the area under the Receiver Operating Characteristic curve (AUC), and weighted kappa statistics are evaluated. Features based on shape encodings of femur, tibia, and menisci plus the performed measurements showed most potential as KOA biomarkers. Differentiation between healthy and severely arthritic knees yielded BAs of up to 99%, 84% were achieved for diagnosis of early KOA. Substantial agreement with weighted kappa values of 0.73, 0.73, and 0.79 were achieved for classification of the grade of medial JSN, lateral JSN, and KLG, respectively. The AUC was 0.60 and 0.75 for prediction of incident KOA and TKR within 5 years, respectively. Quantitative features from automated segmentations yield excellent results for KLG and JSN classification and show potential for incident KOA and TKR prediction. The validity of these features as KOA biomarkers should be further evaluated, especially as extensions of CNN-based approaches. To foster such developments we make all segmentations publicly available together with this publication. Y1 - 2021 U6 - https://doi.org/10.12752/8328 N1 - 46,996 automated segmentations for data from the OAI database. VL - 16 IS - 10 ER - TY - JOUR A1 - Sekuboyina, Anjany A1 - Husseini, Malek E. A1 - Bayat, Amirhossein A1 - Löffler, Maximilian A1 - Liebl, Hans A1 - Li, Hongwei A1 - Tetteh, Giles A1 - Kukačka, Jan A1 - Payer, Christian A1 - Štern, Darko A1 - Urschler, Martin A1 - Chen, Maodong A1 - Cheng, Dalong A1 - Lessmann, Nikolas A1 - Hu, Yujin A1 - Wang, Tianfu A1 - Yang, Dong A1 - Xu, Daguang A1 - Ambellan, Felix A1 - Amiranashvili, Tamaz A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Lehnert, Sebastian A1 - Lirio, Marilia A1 - de Olaguer, Nicolás Pérez A1 - Ramm, Heiko A1 - Sahu, Manish A1 - Tack, Alexander A1 - Zachow, Stefan A1 - Jiang, Tao A1 - Ma, Xinjun A1 - Angerman, Christoph A1 - Wang, Xin A1 - Brown, Kevin A1 - Kirszenberg, Alexandre A1 - Puybareau, Élodie A1 - Chen, Di A1 - Bai, Yiwei A1 - Rapazzo, Brandon H. A1 - Yeah, Timyoas A1 - Zhang, Amber A1 - Xu, Shangliang A1 - Hou, Feng A1 - He, Zhiqiang A1 - Zeng, Chan A1 - Xiangshang, Zheng A1 - Liming, Xu A1 - Netherton, Tucker J. A1 - Mumme, Raymond P. A1 - Court, Laurence E. A1 - Huang, Zixun A1 - He, Chenhang A1 - Wang, Li-Wen A1 - Ling, Sai Ho A1 - Huynh, Lê Duy A1 - Boutry, Nicolas A1 - Jakubicek, Roman A1 - Chmelik, Jiri A1 - Mulay, Supriti A1 - Sivaprakasam, Mohanasankar A1 - Paetzold, Johannes C. A1 - Shit, Suprosanna A1 - Ezhov, Ivan A1 - Wiestler, Benedikt A1 - Glocker, Ben A1 - Valentinitsch, Alexander A1 - Rempfler, Markus A1 - Menze, Björn H. A1 - Kirschke, Jan S. T1 - VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images JF - Medical Image Analysis N2 - Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse. Y1 - 2021 U6 - https://doi.org/10.1016/j.media.2021.102166 VL - 73 ER - TY - JOUR A1 - Sipiran, Ivan A1 - Lazo, Patrick A1 - Lopez, Cristian A1 - Bagewadi, Nihar A1 - Bustos, Benjamin A1 - Dao, Hieu A1 - Gangisetty, Shankar A1 - Hanik, Martin A1 - Ho-Thi, Ngoc-Phuong A1 - Holenderski, Mike A1 - Jarnikov, Dmitri A1 - Labrada, Arniel A1 - Lengauer, Stefan A1 - Licandro, Roxane A1 - Nguyen, Dinh-Huan A1 - Nguyen-Ho, Thang-Long A1 - Pérez Rey, Luis A. A1 - Pham, Bang-Dang A1 - Pham, Minh-Khoi A1 - Preiner, Reinhold A1 - Schreck, Tobias A1 - Trinh, Quoc-Huy A1 - Tonnaer, Loek A1 - von Tycowicz, Christoph A1 - Vu-Le, The-Anh T1 - SHREC 2021: Retrieval of Cultural Heritage Objects JF - Computers and Graphics N2 - This paper presents the methods and results of the SHREC’21 contest on a dataset of cultural heritage (CH) objects. We present a dataset of 938 scanned models that have varied geometry and artistic styles. For the competition, we propose two challenges: the retrieval-by-shape challenge and the retrieval-by-culture challenge. The former aims at evaluating the ability of retrieval methods to discriminate cultural heritage objects by overall shape. The latter focuses on assessing the effectiveness of retrieving objects from the same culture. Both challenges constitute a suitable scenario to evaluate modern shape retrieval methods in a CH domain. Ten groups participated in the contest: thirty runs were submitted for the retrieval-by-shape task, and twenty-six runs were submitted for the retrieval-by-culture challenge. The results show a predominance of learning methods on image-based multi-view representations to characterize 3D objects. Nevertheless, the problem presented in our challenges is far from being solved. We also identify the potential paths for further improvements and give insights into the future directions of research. Y1 - 2021 U6 - https://doi.org/10.1016/j.cag.2021.07.010 VL - 100 SP - 1 EP - 20 ER - TY - JOUR A1 - Moewis, Philippe A1 - Kaiser, René A1 - Trepczynski, Adam A1 - von Tycowicz, Christoph A1 - Krahl, Leonie A1 - Ilg, Ansgar A1 - Holz, Johannes A1 - Duda, Georg T1 - Patient specific resurfacing implant knee surgery in subjects with early osteoarthritis results in medial pivot and lateral femoral rollback during flexion: A retrospective pilot study JF - Knee Surgery, Sports Traumatology, Arthroscopy N2 - Purpose. Metallic resurfacing implants have been developed for the treatment of early, focal, small, condylar and trochlear osteoarthritis (OA) lesions. They represent an option for patients who are either too young to fulfill the criteria for total knee arthroplasty (TKA) or too old for biological treatment. Although relevant clinical evidence has been collected for different resurfacing types, the in vivo post-operative knee kinematics remains unknown. The present study aims to measure and analyse the knee joint kinematics in subjects with patient-specific Episealer implants Methods. Retrospective study design. Fluoroscopic analyses during high flexion activities (unloaded flexion-extension and loaded lunge) were conducted at >12 months post-surgery in ten Episealer knees. The post-operative knee joint kinematics was compared to equally assessed kinematic from ten healthy knees, twenty G-Curve TKA knees and 10 J-Curve knees. Pre- and postoperative clinical data of the Episealer knees were collected using a visual analog scale (VAS), the EQ 5d Health Questionnaire and the Knee Injury and Osteoarthritis Outcome Score (KOOS). Results. During unloaded flexion-extension and loaded lunge, the medial condyle in the Episealer knees remained relative stationary, indicating a medial pivot, while the lateral condyle translated consistently towards posterior. Similarly, reduced movement of the medial condyle and posterior translation of the lateral condyle was also observed in the healthy knees, although to a lesser extent. In contrast, the kinematics of both TKA cohorts during unloaded flexion-extension showed a tendency towards anterior displacement in the medial compartment, which led to significant differences in comparison with the Episealer knees. In the lateral compartment, a certain degree of femoral rollback was noted in the G-Curve TKA cohort. Improved scores were observed in the Episealer subjects between the preoperative and 1-year postoperative follow-up. Conclusion. At 12 months postsurgery, a physiological-like knee kinematics was observed in the group of patient-specific reconstructed chondral/osteochondral lesions by means of a resurfacing Episealer implant strategy. Considering that these patients are physically active and do not fulfill the criteria for TKA, the group is hard to be compared to TKA patients which usually are less active and more challenging. Nevertheless, the comparison to either healthy knee kinematics as well as to TKA reconstructed knees with different implant designs showed a more physiological-like kinematics in the resurfacing implants that seems more appropriate for such a patient group. Despite positive results, careful clinical follow-up of treated patients is recommended for the long-term OA progression. Further investigations need to be encouraged not only in larger patient groups but also in a prospective manner to assess the pre- to postoperative kinematic changes. Y1 - 2021 U6 - https://doi.org/10.1007/s00167-021-06749-8 ER - TY - THES A1 - Punjabi, Dev T1 - Orientation-invariant Dense Correspondence using Graph Convolutional Neural Networks Y1 - 2021 ER - TY - GEN A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - Geodesic B-Score for Improved Assessment of Knee Osteoarthritis N2 - Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination ability over its Euclidean counterpart, which we demonstrate for predictive validity on assessing risks of total knee replacement. This result highlights the potential of the geodesic B-score to enable improved personalized assessment and stratification for interventions. T3 - ZIB-Report - 21-09 KW - Statistical shape analysis KW - Osteoarthritis KW - Geometric statistics KW - Riemannian manifolds Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-81930 SN - 1438-0064 ER - TY - JOUR A1 - Melnyk, Kateryna A1 - Montavon, Grègoire A1 - Klus, Stefan A1 - Conrad, Tim T1 - Graph Kernel Koopman Embedding for Human Microbiome Analysis JF - Applied Network Science N2 - More and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, or some cancer types. Thanks to modern high-throughput omics technologies, it becomes possible to directly analyze human microbiome and its influence on the health status. Microbial communities are monitored over long periods of time and the associations between their members are explored. These relationships can be described by a time-evolving graph. In order to understand responses of the microbial community members to a distinct range of perturbations such as antibiotics exposure or diseases and general dynamical properties, the time-evolving graph of the human microbial communities has to be analyzed. This becomes especially challenging due to dozens of complex interactions among microbes and metastable dynamics. The key to solving this problem is the representation of the time-evolving graphs as fixed-length feature vectors preserving the original dynamics. We propose a method for learning the embedding of the time-evolving graph that is based on the spectral analysis of transfer operators and graph kernels. We demonstrate that our method can capture temporary changes in the time-evolving graph on both synthetic data and real-world data. Our experiments demonstrate the efficacy of the method. Furthermore, we show that our method can be applied to human microbiome data to study dynamic processes. Y1 - 2020 U6 - https://doi.org/10.1007/s41109-020-00339-2 VL - 5 IS - 96 ER -