TY - GEN A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Ehlke, Moritz A1 - Zachow, Stefan T1 - Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative (Supplementary Material) T2 - Medical Image Analysis N2 - We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The shape models and neural networks employed are trained using data of the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets of the SKI10 challenge. For the first time, an accuracy equivalent to the inter-observer variability of human readers has been achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We made the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation. In conclusion, combining statistical anatomical knowledge via SSMs with the localized classification via CNNs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data. Y1 - 2019 U6 - https://doi.org/10.12752/4.ATEZ.1.0 N1 - OAI-ZIB dataset VL - 52 IS - 2 SP - 109 EP - 118 ER - TY - JOUR A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Ehlke, Moritz A1 - Zachow, Stefan T1 - Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative JF - Medical Image Analysis N2 - We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The shape models and neural networks employed are trained using data of the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets of the SKI10 challenge. For the first time, an accuracy equivalent to the inter-observer variability of human readers has been achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We made the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation. In conclusion, combining statistical anatomical knowledge via SSMs with the localized classification via CNNs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data. Y1 - 2019 U6 - https://doi.org/10.1016/j.media.2018.11.009 VL - 52 IS - 2 SP - 109 EP - 118 ER - TY - JOUR A1 - Al Hajj, Hassan A1 - Sahu, Manish A1 - Lamard, Mathieu A1 - Conze, Pierre-Henri A1 - Roychowdhury, Soumali A1 - Hu, Xiaowei A1 - Marsalkaite, Gabija A1 - Zisimopoulos, Odysseas A1 - Dedmari, Muneer Ahmad A1 - Zhao, Fenqiang A1 - Prellberg, Jonas A1 - Galdran, Adrian A1 - Araujo, Teresa A1 - Vo, Duc My A1 - Panda, Chandan A1 - Dahiya, Navdeep A1 - Kondo, Satoshi A1 - Bian, Zhengbing A1 - Bialopetravicius, Jonas A1 - Qiu, Chenghui A1 - Dill, Sabrina A1 - Mukhopadyay, Anirban A1 - Costa, Pedro A1 - Aresta, Guilherme A1 - Ramamurthy, Senthil A1 - Lee, Sang-Woong A1 - Campilho, Aurelio A1 - Zachow, Stefan A1 - Xia, Shunren A1 - Conjeti, Sailesh A1 - Armaitis, Jogundas A1 - Heng, Pheng-Ann A1 - Vahdat, Arash A1 - Cochener, Beatrice A1 - Quellec, Gwenole T1 - CATARACTS: Challenge on Automatic Tool Annotation for cataRACT Surgery JF - Medical Image Analysis N2 - Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future. Y1 - 2019 U6 - https://doi.org/10.1016/j.media.2018.11.008 N1 - Best paper award - Computer Graphics Night 2020 (TU Darmstadt) VL - 52 IS - 2 SP - 24 EP - 41 PB - Elsevier ER - TY - GEN A1 - Tack, Alexander A1 - Zachow, Stefan T1 - Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative N2 - Volumetry of the cartilage of the knee, as needed for the assessment of knee osteoarthritis (KOA), is typically performed in a tedious and subjective process. We present an automated segmentation-based method for the quantification of cartilage volume by employing 3D Convolutional Neural Networks (CNNs). CNNs were trained in a supervised manner using magnetic resonance imaging data as well as cartilage volumetry readings given by clinical experts for 1378 subjects. It was shown that 3D CNNs can be employed for cartilage volumetry with an accuracy similar to expert volumetry readings. In future, accurate automated cartilage volumetry might support both, diagnosis of KOA as well as assessment of KOA progression via longitudinal analysis. T3 - ZIB-Report - 19-05 KW - Deep Learning KW - imaging biomarker KW - radiomics KW - cartilage morphometry KW - volume assessment Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-71439 SN - 1438-0064 ER - TY - JOUR A1 - Hoffmann, Rene A1 - Lemanis, Robert A1 - Wulff, Lena A1 - Zachow, Stefan A1 - Lukeneder, Alexander A1 - Klug, Christian A1 - Keupp, Helmut T1 - Traumatic events in the life of the deep-sea cephalopod mollusc, the coleoid Spirula spirula JF - ScienceDirect: Deep Sea Research Part I - Oceanographic Research N2 - Here, we report on different types of shell pathologies of the enigmatic deep-sea (mesopelagic) cephalopod Spirula spirula. For the first time, we apply non-invasive imaging methods to: document trauma-induced changes in shell shapes, reconstruct the different causes and effects of these pathologies, unravel the etiology, and attempt to quantify the efficiency of the buoyancy apparatus. We have analysed 2D and 3D shell parameters from eleven shells collected as beach findings from the Canary Islands (Gran Canaria and Fuerteventura), West-Australia, and the Maldives. All shells were scanned with a nanotom-m computer tomograph. Seven shells were likely injured by predator attacks: fishes, cephalopods or crustaceans, one specimen was infested by an endoparasite (potentially Digenea) and one shell shows signs of inflammation and one shell shows large fluctuations of chamber volumes without any signs of pathology. These fluctuations are potential indicators of a stressed environment. Pathological shells represent the most deviant morphologies of a single species and can therefore be regarded as morphological end-members. The changes in the shell volume / chamber volume ratio were assessed in order to evaluate the functional tolerance of the buoyancy apparatus showing that these had little effect. Y1 - 2018 U6 - https://doi.org/10.1016/j.dsr.2018.10.007 VL - 142 IS - 12 SP - 127 EP - 144 ER - TY - GEN A1 - Tycowicz, Christoph von A1 - Ambellan, Felix A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - A Riemannian Statistical Shape Model using Differential Coordinates N2 - We propose a novel Riemannian framework for statistical analysis of shapes that is able to account for the nonlinearity in shape variation. By adopting a physical perspective, we introduce a differential representation that puts the local geometric variability into focus. We model these differential coordinates as elements of a Lie group thereby endowing our shape space with a non-Euclidian structure. A key advantage of our framework is that statistics in a manifold shape space become numerically tractable improving performance by several orders of magnitude over state-of-the-art. We show that our Riemannian model is well suited for the identification of intra-population variability as well as inter-population differences. In particular, we demonstrate the superiority of the proposed model in experiments on specificity and generalization ability. We further derive a statistical shape descriptor that outperforms the standard Euclidian approach in terms of shape-based classification of morphological disorders. T3 - ZIB-Report - 16-69 Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-61175 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/6485 SN - 1438-0064 ER - TY - JOUR A1 - Lemanis, Robert A1 - Zachow, Stefan A1 - Hoffmann, René T1 - Comparative cephalopod shell strength and the role of septum morphology on stress distribution JF - PeerJ N2 - The evolution of complexly folded septa in ammonoids has long been a controversial topic. Explanations of the function of these folded septa can be divided into physiological and mechanical hypotheses with the mechanical functions tending to find widespread support. The complexity of the cephalopod shell has made it difficult to directly test the mechanical properties of these structures without oversimplification of the septal morphology or extraction of a small sub-domain. However, the power of modern finite element analysis now permits direct testing of mechanical hypothesis on complete, empirical models of the shells taken from computed tomographic data. Here we compare, for the first time using empirical models, the capability of the shells of extant Nautilus pompilius, Spirula spirula, and the extinct ammonite Cadoceras sp. to withstand hydrostatic pressure and point loads. Results show hydrostatic pressure imparts highest stress on the final septum with the rest of the shell showing minimal compression. S. spirula shows the lowest stress under hydrostatic pressure while N. pompilius shows the highest stress. Cadoceras sp. shows the development of high stress along the attachment of the septal saddles with the shell wall. Stress due to point loads decreases when the point force is directed along the suture as opposed to the unsupported chamber wall. Cadoceras sp. shows the greatest decrease in stress between the point loads compared to all other models. Greater amplitude of septal flutes corresponds with greater stress due to hydrostatic pressure; however, greater amplitude decreases the stress magnitude of point loads directed along the suture. In our models, sutural complexity does not predict greater resistance to hydrostatic pressure but it does seem to increase resistance to point loads, such as would be from predators. This result permits discussion of palaeoecological reconstructions on the basis of septal morphology. We further suggest that the ratio used to characterize septal morphology in the septal strength index and in calculations of tensile strength of nacre are likely insufficient. A better understanding of the material properties of cephalopod nacre may allow the estimation of maximum depth limits of shelled cephalopods through finite element analysis. Y1 - 2016 U6 - https://doi.org/10.7717/peerj.2434 VL - 4 SP - e2434 ER - TY - CHAP A1 - Grewe, Carl Martin A1 - Zachow, Stefan T1 - Fully Automated and Highly Accurate Dense Correspondence for Facial Surfaces T2 - Computer Vision – ECCV 2016 Workshops N2 - We present a novel framework for fully automated and highly accurate determination of facial landmarks and dense correspondence, e.g. a topologically identical mesh of arbitrary resolution, across the entire surface of 3D face models. For robustness and reliability of the proposed approach, we are combining 2D landmark detectors and 3D statistical shape priors with a variational matching method. Instead of matching faces in the spatial domain only, we employ image registration to align the 2D parametrization of the facial surface to a planar template we call the Unified Facial Parameter Domain (ufpd). This allows us to simultaneously match salient photometric and geometric facial features using robust image similarity measures while reasonably constraining geometric distortion in regions with less significant features. We demonstrate the accuracy of the dense correspondence established by our framework on the BU3DFE database with 2500 facial surfaces and show, that our framework outperforms current state-of-the-art methods with respect to the fully automated location of facial landmarks. Y1 - 2016 U6 - https://doi.org/10.1007/978-3-319-48881-3_38 VL - 9914 SP - 552 EP - 568 PB - Springer International Publishing ER - TY - JOUR A1 - Bernard, Florian A1 - Salamanca, Luis A1 - Thunberg, Johan A1 - Tack, Alexander A1 - Jentsch, Dennis A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hertel, Frank A1 - Goncalves, Jorge A1 - Gemmar, Peter T1 - Shape-aware Surface Reconstruction from Sparse Data JF - arXiv N2 - The reconstruction of an object's shape or surface from a set of 3D points is a common topic in materials and life sciences, computationally handled in computer graphics. Such points usually stem from optical or tactile 3D coordinate measuring equipment. Surface reconstruction also appears in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or the alignment of intra-operative navigation and preoperative planning data. In contrast to mere 3D point clouds, medical imaging yields contextual information on the 3D point data that can be used to adopt prior information on the shape that is to be reconstructed from the measurements. In this work we propose to use a statistical shape model (SSM) as a prior for surface reconstruction. The prior knowledge is represented by a point distribution model (PDM) that is associated with a surface mesh. Using the shape distribution that is modelled by the PDM, we reformulate the problem of surface reconstruction from a probabilistic perspective based on a Gaussian Mixture Model (GMM). In order to do so, the given measurements are interpreted as samples of the GMM. By using mixture components with anisotropic covariances that are oriented according to the surface normals at the PDM points, a surface-based tting is accomplished. By estimating the parameters of the GMM in a maximum a posteriori manner, the reconstruction of the surface from the given measurements is achieved. Extensive experiments suggest that our proposed approach leads to superior surface reconstructions compared to Iterative Closest Point (ICP) methods. Y1 - 2016 SP - 1602.08425v1 ER - TY - JOUR A1 - Bernard, Florian A1 - Salamanca, Luis A1 - Thunberg, Johan A1 - Tack, Alexander A1 - Jentsch, Dennis A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hertel, Frank A1 - Goncalves, Jorge A1 - Gemmar, Peter T1 - Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds JF - Medical Image Analysis N2 - The reconstruction of an object’s shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process of aligning intra-operative navigation and preoperative planning data. In such scenarios, one usually has to deal with sparse data, which significantly aggravates the problem of reconstruction. However, medical applications often provide contextual information about the 3D point data that allow to incorporate prior knowledge about the shape that is to be reconstructed. To this end, we propose the use of a statistical shape model (SSM) as a prior for surface reconstruction. The SSM is represented by a point distribution model (PDM), which is associated with a surface mesh. Using the shape distribution that is modelled by the PDM, we formulate the problem of surface reconstruction from a probabilistic perspective based on a Gaussian Mixture Model (GMM). In order to do so, the given points are interpreted as samples of the GMM. By using mixture components with anisotropic covariances that are “oriented” according to the surface normals at the PDM points, a surface-based fitting is accomplished. Estimating the parameters of the GMM in a maximum a posteriori manner yields the reconstruction of the surface from the given data points. We compare our method to the extensively used Iterative Closest Points method on several different anatomical datasets/SSMs (brain, femur, tibia, hip, liver) and demonstrate superior accuracy and robustness on sparse data. Y1 - 2017 UR - http://www.sciencedirect.com/science/article/pii/S1361841517300233 U6 - https://doi.org/10.1016/j.media.2017.02.005 VL - 38 SP - 77 EP - 89 ER - TY - JOUR A1 - Gladilin, Evgeny A1 - Zachow, Stefan A1 - Deuflhard, Peter A1 - Hege, Hans-Christian T1 - A nonlinear elastic soft tissue model for craniofacial surgery simulations JF - ESAIM, Proc. Y1 - 2002 U6 - https://doi.org/10.1051/proc:2002011 VL - 12 SP - 61 EP - 66 ER - TY - CHAP A1 - Gladilin, Evgeny A1 - Zachow, Stefan A1 - Deuflhard, Peter A1 - Hege, Hans-Christian T1 - A nonlinear soft tissue model for craniofacial surgery simulations T2 - Proc. of Modeling and Simulation for Computer-aided Medicine and Surgery (MS4CMS Y1 - 2002 PB - INRIA CY - Paris, France ER - TY - CHAP A1 - Gladilin, Evgeny A1 - Zachow, Stefan A1 - Deuflhard, Peter A1 - Hege, Hans-Christian T1 - Biomechanical modeling of individual facial emotion expressions T2 - Proc. of Visualization, Imaging, and Image Processing (VIIP) Y1 - 2002 SP - 7 EP - 11 CY - Malaga, Spain ER - TY - CHAP A1 - Gladilin, Evgeny A1 - Zachow, Stefan A1 - Deuflhard, Peter A1 - Hege, Hans-Christian ED - Meiler, M. ED - Saupe, D. ED - Krugel, F. ED - Handels, H. ED - Lehmann, T. T1 - Biomechanisches Modell zur Abschätzung der individuellen Gesichtsmimik T2 - Proc.of Workshop Bildverarbeitung für die Medizin (BVM) Y1 - 2002 SP - 25 EP - 28 CY - Leipzig, Germany ER - TY - CHAP A1 - Zachow, Stefan A1 - Weiser, Martin A1 - Hege, Hans-Christian A1 - Deuflhard, Peter ED - Payan, Y. T1 - Soft Tissue Prediction in Computer Assisted Maxillofacial Surgery Planning T2 - Biomechanics Applied to Computer Assisted Surgery Y1 - 2005 SP - 277 EP - 298 PB - Research Signpost ER - TY - CHAP A1 - Zachow, Stefan A1 - Weiser, Martin A1 - Deuflhard, Peter ED - Niederlag, Wolfgang ED - Lemke, Heinz ED - Meixensberger, Jürgen ED - Baumann, Michael T1 - Modellgestützte Operationsplanung in der Kopfchirurgie T2 - Modellgestützte Therapie Y1 - 2008 SP - 140 EP - 156 PB - Health Academy ER - TY - JOUR A1 - Zeilhofer, Hans-Florian A1 - Zachow, Stefan A1 - Fairley, Jeffrey A1 - Sader, Robert A1 - Deuflhard, Peter T1 - Treatment Planning and Simulation in Craniofacial Surgery with Virtual Reality Techiques JF - Journal of Cranio-Maxillofacial Surgery Y1 - 2000 VL - 28 (Suppl. 1) SP - 82 ER - TY - JOUR A1 - Taylor, William R. A1 - Pöpplau, Berry M. A1 - König, Christian A1 - Ehrig, Rainald A1 - Zachow, Stefan A1 - Duda, Georg A1 - Heller, Markus O. T1 - The medial-lateral force distribution in the ovine stifle joint during walking JF - Journal of Orthopaedic Research Y1 - 2011 U6 - https://doi.org/10.1002/jor.21254 VL - 29 IS - 4 SP - 567 EP - 571 ER - TY - JOUR A1 - Lamas-Rodríguez, Julián A1 - Heras, Dora Blanco A1 - Argüello, Francisco A1 - Kainmüller, Dagmar A1 - Zachow, Stefan A1 - Bóo, Montserrat T1 - GPU-accelerated level-set segmentation JF - Journal of Real-Time Image Processing Y1 - 2013 UR - http://dx.doi.org/10.1007/s11554-013-0378-6 U6 - https://doi.org/10.1007/s11554-013-0378-6 SN - 1861-8200 SP - 1 EP - 15 PB - Springer Berlin Heidelberg ER - TY - JOUR A1 - Hoffmann, René A1 - Schultz, Julia A. A1 - Schellhorn, Rico A1 - Rybacki, Erik A1 - Keupp, Helmut A1 - Gerden, S. R. A1 - Lemanis, Robert A1 - Zachow, Stefan T1 - Non-invasive imaging methods applied to neo- and paleontological cephalopod research JF - Biogeosciences N2 - Several non-invasive methods are common practice in natural sciences today. Here we present how they can be applied and contribute to current topics in cephalopod (paleo-) biology. Different methods will be compared in terms of time necessary to acquire the data, amount of data, accuracy/resolution, minimum/maximum size of objects that can be studied, the degree of post-processing needed and availability. The main application of the methods is seen in morphometry and volumetry of cephalopod shells. In particular we present a method for precise buoyancy calculation. Therefore, cephalopod shells were scanned together with different reference bodies, an approach developed in medical sciences. It is necessary to know the volume of the reference bodies, which should have similar absorption properties like the object of interest. Exact volumes can be obtained from surface scanning. Depending on the dimensions of the study object different computed tomography techniques were applied. Y1 - 2014 U6 - https://doi.org/10.5194/bg-11-2721-2014 N1 - To access the corresponding discussion paper go to www.biogeosciences-discuss.net/10/18803/2013/ - Biogeosciences Discuss., 10, 18803-18851, 2013 VL - 11 IS - 10 SP - 2721 EP - 2739 ER - TY - CHAP A1 - Ramm, Heiko A1 - Victoria Morillo, Oscar Salvador A1 - Todt, Ingo A1 - Schirmacher, Hartmut A1 - Ernst, Arneborg A1 - Zachow, Stefan A1 - Lamecker, Hans ED - Freysinger, Wolfgang T1 - Visual Support for Positioning Hearing Implants T2 - Proceedings of the 12th annual meeting of the CURAC society Y1 - 2013 SP - 116 EP - 120 ER - TY - JOUR A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Heller, Markus O. A1 - Weber, Britta A1 - Hege, Hans-Christian A1 - Zachow, Stefan T1 - Omnidirectional Displacements for Deformable Surfaces JF - Medical Image Analysis Y1 - 2013 U6 - https://doi.org/10.1016/j.media.2012.11.006 VL - 17 IS - 4 SP - 429 EP - 441 PB - Elsevier ER - TY - JOUR A1 - Dunlop, Jason A1 - Apanaskevich, Dmitry A1 - Lehmann, Jens A1 - Hoffmann, Rene A1 - Fusseis, Florian A1 - Ehlke, Moritz A1 - Zachow, Stefan A1 - Xiao, Xianghui T1 - Microtomography of the Baltic amber tick Ixodes succineus reveals affinities with the modern Asian disease vector Ixodes ovatus JF - BMC Evolutionary Biology N2 - Background: Fossil ticks are extremely rare, whereby Ixodes succineus Weidner, 1964 from Eocene (ca. 44-49 Ma) Baltic amber is one of the oldest examples of a living hard tick genus (Ixodida: Ixodidae). Previous work suggested it was most closely related to the modern and widespread European sheep tick Ixodes ricinus (Linneaus, 1758). Results: Restudy using phase contrast synchrotron x-ray tomography yielded images of exceptional quality. These confirm the fossil's referral to Ixodes Latreille, 1795, but the characters resolved here suggest instead affinities with the Asian subgenus Partipalpiger Hoogstraal et al., 1973 and its single living (and medically significant) species Ixodes ovatus Neumann, 1899. We redescribe the amber fossil here as Ixodes (Partipalpiger) succineus. Conclusions: Our data suggest that Ixodes ricinus is unlikely to be directly derived from Weidner's amber species, but instead reveals that the Partipalpiger lineage was originally more widely distributed across the northern hemisphere. The closeness of Ixodes (P.) succineus to a living vector of a wide range of pathogens offers the potential to correlate its spatial and temporal position (northern Europe, nearly 50 million years ago) with the estimated origination dates of various tick-borne diseases. Y1 - 2016 U6 - https://doi.org/10.1186/s12862-016-0777-y VL - 16 IS - 1 ER - TY - JOUR A1 - Schenkl, Sebastian A1 - Muggenthaler, Holger A1 - Hubig, Michael A1 - Erdmann, Bodo A1 - Weiser, Martin A1 - Zachow, Stefan A1 - Heinrich, Andreas A1 - Güttler, Felix Victor A1 - Teichgräber, Ulf A1 - Mall, Gita T1 - Automatic CT-based finite element model generation for temperature-based death time estimation: feasibility study and sensitivity analysis JF - International Journal of Legal Medicine N2 - Temperature based death time estimation is based either on simple phenomenological models of corpse cooling or on detailed physical heat transfer models. The latter are much more complex, but allow a higher accuracy of death time estimation as in principle all relevant cooling mechanisms can be taken into account. Here, a complete work flow for finite element based cooling simulation models is presented. The following steps are demonstrated on CT-phantoms: • CT-scan • Segmentation of the CT images for thermodynamically relevant features of individual geometries • Conversion of the segmentation result into a Finite Element (FE) simulation model • Computation of the model cooling curve • Calculation of the cooling time For the first time in FE-based cooling time estimation the steps from the CT image over segmentation to FE model generation are semi-automatically performed. The cooling time calculation results are compared to cooling measurements performed on the phantoms under controlled conditions. In this context, the method is validated using different CTphantoms. Some of the CT phantoms thermodynamic material parameters had to be experimentally determined via independent experiments. Moreover the impact of geometry and material parameter uncertainties on the estimated cooling time is investigated by a sensitivity analysis. KW - temperature based death time estimation KW - finite element method KW - CT segmentation KW - sensitivity analysis Y1 - 2017 U6 - https://doi.org/doi:10.1007/s00414-016-1523-0 VL - 131 IS - 3 SP - 699 EP - 712 ER - TY - CHAP A1 - Joachimsky, Robert A1 - Ambellan, Felix A1 - Zachow, Stefan T1 - Computerassistierte Auswahl und Platzierung von interpositionalen Spacern zur Behandlung früher Gonarthrose T2 - Proceedings of the Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC) N2 - Degenerative Gelenkerkrankungen, wie die Osteoarthrose, sind ein häufiges Krankheitsbild unter älteren Erwachsenen. Hierbei verringert sich u.a. der Gelenkspalt aufgrund degenerierten Knorpels oder geschädigter Menisci. Ein in den Gelenkspalt eingebrachter interpositionaler Spacer soll die mit der Osteoarthrose einhergehende verringerte Gelenkkontaktfläche erhöhen und so der teilweise oder vollständige Gelenkersatz hinausgezögert oder vermieden werden. In dieser Arbeit präsentieren wir eine Planungssoftware für die Auswahl und Positionierung eines interpositionalen Spacers am Patientenmodell. Auf einer MRT-basierten Bildsegmentierung aufbauend erfolgt eine geometrische Rekonstruktion der 3D-Anatomie des Kniegelenks. Anhand dieser wird der Gelenkspalt bestimmt, sowie ein Spacer ausgewählt und algorithmisch vorpositioniert. Die Positionierung des Spacers ist durch den Benutzer jederzeit interaktiv anpassbar. Für jede Positionierung eines Spacers wird ein Fitness-Wert zur Knieanatomie des jeweiligen Patienten berechnet und den Nutzern Rückmeldung hinsichtlich Passgenauigkeit gegeben. Die Software unterstützt somit als Entscheidungshilfe die behandelnden Ärzte bei der patientenspezifischen Spacerauswahl. KW - Osteoarthrose KW - Kniegelenk KW - Medizinische Planung KW - Visualisierung Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-65321 VL - 16 SP - 106 EP - 111 ER - TY - CHAP A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Wilson, Dave A1 - Anglin, Carolyn A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data T2 - Proceedings of the Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC) N2 - In this study we investigate methods for fitting a Statistical Shape Model (SSM) to intraoperatively acquired point cloud data from a surgical navigation system. We validate the fitted models against the pre-operatively acquired Magnetic Resonance Imaging (MRI) data from the same patients. We consider a cohort of 10 patients who underwent navigated total knee arthroplasty. As part of the surgical protocol the patients’ distal femurs were partially digitized. All patients had an MRI scan two months pre-operatively. The MRI data were manually segmented and the reconstructed bone surfaces used as ground truth against which the fit was compared. Two methods were used to fit the SSM to the data, based on (1) Iterative Closest Points (ICP) and (2) Gaussian Mixture Models (GMM). For both approaches, the difference between model fit and ground truth surface averaged less than 1.7 mm and excellent correspondence with the distal femoral morphology can be demonstrated. KW - Total Knee Arthoplasty KW - Sparse Geometry Reconstruction KW - Statistical Shape Models Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-65339 VL - 16 SP - 24 EP - 30 ER - TY - GEN A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Wilson, Dave A1 - Anglin, Carolyn A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data N2 - In this study we investigate methods for fitting a Statistical Shape Model (SSM) to intraoperatively acquired point cloud data from a surgical navigation system. We validate the fitted models against the pre-operatively acquired Magnetic Resonance Imaging (MRI) data from the same patients. We consider a cohort of 10 patients who underwent navigated total knee arthroplasty. As part of the surgical protocol the patients’ distal femurs were partially digitized. All patients had an MRI scan two months pre-operatively. The MRI data were manually segmented and the reconstructed bone surfaces used as ground truth against which the fit was compared. Two methods were used to fit the SSM to the data, based on (1) Iterative Closest Points (ICP) and (2) Gaussian Mixture Models (GMM). For both approaches, the difference between model fit and ground truth surface averaged less than 1.7 mm and excellent correspondence with the distal femoral morphology can be demonstrated. T3 - ZIB-Report - 17-71 KW - Knee Arthroplasty KW - Sparse Geometry Reconstruction KW - Statistical Shape Models Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-66052 SN - 1438-0064 ER - TY - CHAP A1 - Grewe, Carl Martin A1 - le Roux, Gabriel A1 - Pilz, Sven-Kristofer A1 - Zachow, Stefan T1 - Spotting the Details: The Various Facets of Facial Expressions T2 - IEEE International Conference on Automatic Face and Gesture Recognition Y1 - 2018 U6 - https://doi.org/10.1109/FG.2018.00049 SP - 286 EP - 293 ER - TY - GEN A1 - Sahu, Manish A1 - Dill, Sabrina A1 - Mukhopadyay, Anirban A1 - Zachow, Stefan T1 - Surgical Tool Presence Detection for Cataract Procedures N2 - This article outlines the submission to the CATARACTS challenge for automatic tool presence detection [1]. Our approach for this multi-label classification problem comprises labelset-based sampling, a CNN architecture and temporal smothing as described in [3], which we call ZIB-Res-TS. T3 - ZIB-Report - 18-28 Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-69110 SN - 1438-0064 ER - TY - JOUR A1 - Tack, Alexander A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative JF - Osteoarthritis and Cartilage N2 - Abstract: Objective: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof. Method: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MOAKS experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression. Results: Segmentation accuracy measured as Dice Similarity Coefficient was 83.8% for medial menisci (MM) and 88.9% for lateral menisci (LM) at baseline, and 83.1% and 88.3% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (ρ=0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16±0.93 mm vs. 0.83±0.92 mm; p<0.05). Conclusion: Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA. Y1 - 2018 U6 - https://doi.org/10.1016/j.joca.2018.02.907 VL - 26 IS - 5 SP - 680 EP - 688 ER - TY - GEN A1 - Tack, Alexander A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative (Supplementary Material) N2 - Abstract: Objective: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof. Method: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MOAKS experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression. Results: Segmentation accuracy measured as Dice Similarity Coefficient was 83.8% for medial menisci (MM) and 88.9% for lateral menisci (LM) at baseline, and 83.1% and 88.3% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (ρ=0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16±0.93 mm vs. 0.83±0.92 mm; p<0.05). Conclusion: Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA. Y1 - 2018 U6 - https://doi.org/10.12752/4.TMZ.1.0 N1 - Supplementary data to reproduce results from the related publication, including convolutional neural networks' weights. ER - TY - CHAP A1 - Krämer, Martin A1 - Maggioni, Marta A1 - Tycowicz, Christoph von A1 - Brisson, Nick A1 - Zachow, Stefan A1 - Duda, Georg A1 - Reichenbach, Jürgen T1 - Ultra-short echo-time (UTE) imaging of the knee with curved surface reconstruction-based extraction of the patellar tendon T2 - ISMRM (International Society for Magnetic Resonance in Medicine), 26th Annual Meeting 2018, Paris, France N2 - Due to very short T2 relaxation times, imaging of tendons is typically performed using ultra-short echo-time (UTE) acquisition techniques. In this work, we combined an echo-train shifted multi-echo 3D UTE imaging sequence with a 3D curved surface reconstruction to virtually extract the patellar tendon from an acquired 3D UTE dataset. Based on the analysis of the acquired multi-echo data, a T2* relaxation time parameter map was calculated and interpolated to the curved surface of the patellar tendon. Y1 - 2018 ER - TY - CHAP A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Ehlke, Moritz A1 - Zachow, Stefan T1 - Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative T2 - Medical Imaging with Deep Learning N2 - We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging, that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The method is evaluated on data of the MICCAI grand challenge "Segmentation of Knee Images 2010". For the first time an accuracy equivalent to the inter-observer variability of human readers has been achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy. In conclusion, combining of anatomical knowledge using SSMs with localized classification via CNNs results in a state-of-the-art segmentation method. Y1 - 2018 ER - TY - JOUR A1 - Brüning, Jan A1 - Hildebrandt, Thomas A1 - Heppt, Werner A1 - Schmidt, Nora A1 - Lamecker, Hans A1 - Szengel, Angelika A1 - Amiridze, Natalja A1 - Ramm, Heiko A1 - Bindernagel, Matthias A1 - Zachow, Stefan A1 - Goubergrits, Leonid T1 - Characterization of the Airflow within an Average Geometry of the Healthy Human Nasal Cavity JF - Scientific Reports N2 - This study’s objective was the generation of a standardized geometry of the healthy nasal cavity. An average geometry of the healthy nasal cavity was generated using a statistical shape model based on 25 symptom-free subjects. Airflow within the average geometry and these geometries was calculated using fluid simulations. Integral measures of the nasal resistance, wall shear stresses (WSS) and velocities were calculated as well as cross-sectional areas (CSA). Furthermore, individual WSS and static pressure distributions were mapped onto the average geometry. The average geometry featured an overall more regular shape that resulted in less resistance, reduced wall shear stresses and velocities compared to the median of the 25 geometries. Spatial distributions of WSS and pressure of average geometry agreed well compared to the average distributions of all individual geometries. The minimal CSA of the average geometry was larger than the median of all individual geometries (83.4 vs. 74.7 mm²). The airflow observed within the average geometry of the healthy nasal cavity did not equal the average airflow of the individual geometries. While differences observed for integral measures were notable, the calculated values for the average geometry lay within the distributions of the individual parameters. Spatially resolved parameters differed less prominently. Y1 - 2020 UR - https://rdcu.be/b2irD U6 - https://doi.org/10.1038/s41598-020-60755-3 VL - 3755 IS - 10 ER - TY - CHAP A1 - Sahu, Manish A1 - Strömsdörfer, Ronja A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation T2 - Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part III N2 - Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems. Recent success of image-based solutions using fully-supervised deep learning approaches can be attributed to the collection of big labeled datasets. However, the annotation of a big dataset of real videos can be prohibitively expensive and time consuming. Computer simulations could alleviate the manual labeling problem, however, models trained on simulated data do not generalize to real data. This work proposes a consistency-based framework for joint learning of simulated and real (unlabeled) endoscopic data to bridge this performance generalization issue. Empirical results on two data sets (15 videos of the Cholec80 and EndoVis'15 dataset) highlight the effectiveness of the proposed Endo-Sim2Real method for instrument segmentation. We compare the segmentation of the proposed approach with state-of-the-art solutions and show that our method improves segmentation both in terms of quality and quantity. Y1 - 2020 U6 - https://doi.org/https://doi.org/10.1007/978-3-030-59716-0_75 VL - 12263 PB - Springer Nature ER - TY - JOUR A1 - Sahu, Manish A1 - Szengel, Angelika A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Surgical phase recognition by learning phase transitions JF - Current Directions in Biomedical Engineering (CDBME) N2 - Automatic recognition of surgical phases is an important component for developing an intra-operative context-aware system. Prior work in this area focuses on recognizing short-term tool usage patterns within surgical phases. However, the difference between intra- and inter-phase tool usage patterns has not been investigated for automatic phase recognition. We developed a Recurrent Neural Network (RNN), in particular a state-preserving Long Short Term Memory (LSTM) architecture to utilize the long-term evolution of tool usage within complete surgical procedures. For fully automatic tool presence detection from surgical video frames, a Convolutional Neural Network (CNN) based architecture namely ZIBNet is employed. Our proposed approach outperformed EndoNet by 8.1% on overall precision for phase detection tasks and 12.5% on meanAP for tool recognition tasks. Y1 - 2020 U6 - https://doi.org/https://doi.org/10.1515/cdbme-2020-0037 N1 - Nomination for the Best-Paper Award VL - 6 IS - 1 SP - 20200037 PB - De Gruyter ER - TY - GEN A1 - Sahu, Manish A1 - Szengel, Angelika A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Analyzing laparoscopic cholecystectomy with deep learning: automatic detection of surgical tools and phases T2 - 28th International Congress of the European Association for Endoscopic Surgery (EAES) N2 - Motivation: The ever-rising volume of patients, high maintenance cost of operating rooms and time consuming analysis of surgical skills are fundamental problems that hamper the practical training of the next generation of surgeons. The hospitals prefer to keep the surgeons busy in real operations over training young surgeons for obvious economic reasons. One fundamental need in surgical training is the reduction of the time needed by the senior surgeon to review the endoscopic procedures performed by the young surgeon while minimizing the subjective bias in evaluation. The unprecedented performance of deep learning ushers the new age of data-driven automatic analysis of surgical skills. Method: Deep learning is capable of efficiently analyzing thousands of hours of laparoscopic video footage to provide an objective assessment of surgical skills. However, the traditional end-to-end setting of deep learning (video in, skill assessment out) is not explainable. Our strategy is to utilize the surgical process modeling framework to divide the surgical process into understandable components. This provides the opportunity to employ deep learning for superior yet automatic detection and evaluation of several aspects of laparoscopic cholecystectomy such as surgical tool and phase detection. We employ ZIBNet for the detection of surgical tool presence. ZIBNet employs pre-processing based on tool usage imbalance, a transfer learned 50-layer residual network (ResNet-50) and temporal smoothing. To encode the temporal evolution of tool usage (over the entire video sequence) that relates to the surgical phases, Long Short Term Memory (LSTM) units are employed with long-term dependency. Dataset: We used CHOLEC 80 dataset that consists of 80 videos of laparoscopic cholecystectomy performed by 13 surgeons, divided equally for training and testing. In these videos, up to three different tools (among 7 types of tools) can be present in a frame. Results: The mean average precision of the detection of all tools is 93.5 ranging between 86.8 and 99.3, a significant improvement (p <0.01) over the previous state-of-the-art. We observed that less frequent tools like Scissors, Irrigator, Specimen Bag etc. are more related to phase transitions. The overall precision (recall) of the detection of all surgical phases is 79.6 (81.3). Conclusion: While this is not the end goal for surgical skill analysis, the development of such a technological platform is essential toward a data-driven objective understanding of surgical skills. In future, we plan to investigate surgeon-in-the-loop analysis and feedback for surgical skill analysis. Y1 - 2020 UR - https://academy.eaes.eu/eaes/2020/28th/298882/manish.sahu.analyzing.laparoscopic.cholecystectomy.with.deep.learning.html?f=listing%3D0%2Abrowseby%3D8%2Asortby%3D2 ER - TY - JOUR A1 - Pimentel, Pedro A1 - Szengel, Angelika A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Estacio, Laura A1 - Doenitz, Christian A1 - Ramm, Heiko ED - Li, Jianning ED - Egger, Jan T1 - Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks BT - First Challenge, AutoImplant 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings JF - Towards the Automatization of Cranial Implant Design in Cranioplasty N2 - We present an automated method for extrapolating missing regions in label data of the skull in an anatomically plausible manner. The ultimate goal is to design patient-speci� c cranial implants for correcting large, arbitrarily shaped defects of the skull that can, for example, result from trauma of the head. Our approach utilizes a 3D statistical shape model (SSM) of the skull and a 2D generative adversarial network (GAN) that is trained in an unsupervised fashion from samples of healthy patients alone. By � tting the SSM to given input labels containing the skull defect, a First approximation of the healthy state of the patient is obtained. The GAN is then applied to further correct and smooth the output of the SSM in an anatomically plausible manner. Finally, the defect region is extracted using morphological operations and subtraction between the extrapolated healthy state of the patient and the defective input labels. The method is trained and evaluated based on data from the MICCAI 2020 AutoImplant challenge. It produces state-of-the art results on regularly shaped cut-outs that were present in the training and testing data of the challenge. Furthermore, due to unsupervised nature of the approach, the method generalizes well to previously unseen defects of varying shapes that were only present in the hidden test dataset. Y1 - 2020 U6 - https://doi.org/10.1007/978-3-030-64327-0_3 N1 - Best Paper Award VL - 12439 SP - 16 EP - 27 PB - Springer International Publishing ET - 1 ER - TY - GEN A1 - Grewe, Carl Martin A1 - Zachow, Stefan ED - Doll, Nikola ED - Bredekamp, Horst ED - Schäffner, Wolfgang T1 - Face to Face-Interface T2 - +ultra. Knowledge & Gestaltung Y1 - 2017 SP - 320 EP - 321 PB - Seemann Henschel ER - TY - GEN A1 - Grewe, Carl Martin A1 - Le Roux, Gabriel A1 - Pilz, Sven-Kristofer A1 - Zachow, Stefan T1 - Spotting the Details: The Various Facets of Facial Expressions N2 - 3D Morphable Models (MM) are a popular tool for analysis and synthesis of facial expressions. They represent plausible variations in facial shape and appearance within a low-dimensional parameter space. Fitted to a face scan, the model's parameters compactly encode its expression patterns. This expression code can be used, for instance, as a feature in automatic facial expression recognition. For accurate classification, an MM that can adequately represent the various characteristic facets and variants of each expression is necessary. Currently available MMs are limited in the diversity of expression patterns. We present a novel high-quality Facial Expression Morphable Model built from a large-scale face database as a tool for expression analysis and synthesis. Establishment of accurate dense correspondence, up to finest skin features, enables a detailed statistical analysis of facial expressions. Various characteristic shape patterns are identified for each expression. The results of our analysis give rise to a new facial expression code. We demonstrate the advantages of such a code for the automatic recognition of expressions, and compare the accuracy of our classifier to state-of-the-art. T3 - ZIB-Report - 18-06 Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-67696 SN - 1438-0064 ER - TY - JOUR A1 - Oeltze-Jaffra, Steffen A1 - Meuschke, Monique A1 - Neugebauer, Mathias A1 - Saalfeld, Sylvia A1 - Lawonn, Kai A1 - Janiga, Gabor A1 - Hege, Hans-Christian A1 - Zachow, Stefan A1 - Preim, Bernhard T1 - Generation and Visual Exploration of Medical Flow Data: Survey, Research Trends, and Future Challenges JF - Computer Graphics Forum N2 - Simulations and measurements of blood and air flow inside the human circulatory and respiratory system play an increasingly important role in personalized medicine for prevention, diagnosis, and treatment of diseases. This survey focuses on three main application areas. (1) Computational Fluid Dynamics (CFD) simulations of blood flow in cerebral aneurysms assist in predicting the outcome of this pathologic process and of therapeutic interventions. (2) CFD simulations of nasal airflow allow for investigating the effects of obstructions and deformities and provide therapy decision support. (3) 4D Phase-Contrast (4D PC) Magnetic Resonance Imaging (MRI) of aortic hemodynamics supports the diagnosis of various vascular and valve pathologies as well as their treatment. An investigation of the complex and often dynamic simulation and measurement data requires the coupling of sophisticated visualization, interaction, and data analysis techniques. In this paper, we survey the large body of work that has been conducted within this realm. We extend previous surveys by incorporating nasal airflow, addressing the joint investigation of blood flow and vessel wall properties, and providing a more fine-granular taxonomy of the existing techniques. From the survey, we extract major research trends and identify open problems and future challenges. The survey is intended for researchers interested in medical flow but also more general, in the combined visualization of physiology and anatomy, the extraction of features from flow field data and feature-based visualization, the visual comparison of different simulation results, and the interactive visual analysis of the flow field and derived characteristics. Y1 - 2019 U6 - https://doi.org/10.1111/cgf.13394 VL - 38 IS - 1 SP - 87 EP - 125 PB - Wiley ER - TY - GEN A1 - Tack, Alexander A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative N2 - Abstract: Objective: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof. Method: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MOAKS experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression. Results: Segmentation accuracy measured as Dice Similarity Coefficient was 83.8% for medial menisci (MM) and 88.9% for lateral menisci (LM) at baseline, and 83.1% and 88.3% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (ρ=0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16±0.93 mm vs. 0.83±0.92 mm; p<0.05). Conclusion: Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA. T3 - ZIB-Report - 18-15 Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-68038 SN - 1438-0064 VL - 26 IS - 5 SP - 680 EP - 688 ER - TY - JOUR A1 - Moldenhauer, Marian A1 - Weiser, Martin A1 - Zachow, Stefan T1 - Adaptive Algorithms for Optimal Hip Implant Positioning JF - PAMM N2 - In an aging society where the number of joint replacements rises, it is important to also increase the longevity of implants. In particular hip implants have a lifetime of at most 15 years. This derives primarily from pain due to implant migration, wear, inflammation, and dislocation, which is affected by the positioning of the implant during the surgery. Current joint replacement practice uses 2D software tools and relies on the experience of surgeons. Especially the 2D tools fail to take the patients’ natural range of motion as well as stress distribution in the 3D joint induced by different daily motions into account. Optimizing the hip joint implant position for all possible parametrized motions under the constraint of a contact problem is prohibitively expensive as there are too many motions and every position change demands a recalculation of the contact problem. For the reduction of the computational effort, we use adaptive refinement on the parameter domain coupled with the interpolation method of Kriging. A coarse initial grid is to be locally refined using goal-oriented error estimation, reducing locally high variances. This approach will be combined with multi-grid optimization such that numerical errors are reduced. Y1 - 2017 U6 - https://doi.org/10.1002/pamm.201710071 VL - 17 IS - 1 SP - 203 EP - 204 ER - TY - JOUR A1 - Weiser, Martin A1 - Erdmann, Bodo A1 - Schenkl, Sebastian A1 - Muggenthaler, Holger A1 - Hubig, Michael A1 - Mall, Gita A1 - Zachow, Stefan T1 - Uncertainty in Temperature-Based Determination of Time of Death JF - Heat and Mass Transfer N2 - Temperature-based estimation of time of death (ToD) can be per- formed either with the help of simple phenomenological models of corpse cooling or with detailed mechanistic (thermodynamic) heat transfer mod- els. The latter are much more complex, but allow a higher accuracy of ToD estimation as in principle all relevant cooling mechanisms can be taken into account. The potentially higher accuracy depends on the accuracy of tissue and environmental parameters as well as on the geometric resolution. We in- vestigate the impact of parameter variations and geometry representation on the estimated ToD based on a highly detailed 3D corpse model, that has been segmented and geometrically reconstructed from a computed to- mography (CT) data set, differentiating various organs and tissue types. Y1 - 2018 U6 - https://doi.org/10.1007/s00231-018-2324-4 VL - 54 IS - 9 SP - 2815 EP - 2826 PB - Springer ER - TY - CHAP A1 - Neumann, Mario A1 - Hellwich, Olaf A1 - Zachow, Stefan T1 - Localization and Classification of Teeth in Cone Beam CT using Convolutional Neural Networks T2 - Proc. of the 18th annual conference on Computer- and Robot-assisted Surgery (CURAC) N2 - In dentistry, software-based medical image analysis and visualization provide efficient and accurate diagnostic and therapy planning capabilities. We present an approach for the automatic recognition of tooth types and positions in digital volume tomography (DVT). By using deep learning techniques in combination with dimensionality reduction through non-planar reformatting of the jaw anatomy, DVT data can be efficiently processed and teeth reliably recognized and classified, even in the presence of imaging artefacts, missing or dislocated teeth. We evaluated our approach, which is based on 2D Convolutional Neural Networks (CNNs), on 118 manually annotated cases of clinical DVT datasets. Our proposed method correctly classifies teeth with an accuracy of 94% within a limit of 2mm distance to ground truth labels. Y1 - 2019 SN - 978-3-00-063717-9 SP - 182 EP - 188 ER - TY - CHAP A1 - Joachimsky, Robert A1 - Ma, Lihong A1 - Icking, Christian A1 - Zachow, Stefan T1 - A Collision-Aware Articulated Statistical Shape Model of the Human Spine T2 - Proc. of the 18th annual conference on Computer- and Robot-assisted Surgery (CURAC) N2 - Statistical Shape Models (SSMs) are a proven means for model-based 3D anatomy reconstruction from medical image data. In orthopaedics and biomechanics, SSMs are increasingly employed to individualize measurement data or to create individualized anatomical models to which implants can be adapted to or functional tests can be performed on. For modeling and analysis of articulated structures, so called articulated SSMs (aSSMs) have been developed. However, a missing feature of aSSMs is the consideration of collisions in the course of individual fitting and articulation. The aim of our work was to develop aSSMs that handle collisions between components correctly. That way it becomes possible to adjust shape and articulation in view of a physically and geometrically plausible individualization. To be able to apply collision-aware aSSMs in simulation and optimisation, our approach is based on an e� cient collision detection method employing Graphics Processing Units (GPUs). Y1 - 2019 SP - 58 EP - 64 ER - TY - JOUR A1 - Krämer, Martin A1 - Maggioni, Marta A1 - Brisson, Nicholas A1 - Zachow, Stefan A1 - Teichgräber, Ulf A1 - Duda, Georg A1 - Reichenbach, Jürgen T1 - T1 and T2* mapping of the human quadriceps and patellar tendons using ultra-short echo-time (UTE) imaging and bivariate relaxation parameter-based volumetric visualization JF - Magnetic Resonance Imaging N2 - Quantification of magnetic resonance (MR)-based relaxation parameters of tendons and ligaments is challenging due to their very short transverse relaxation times, requiring application of ultra-short echo-time (UTE) imaging sequences. We quantify both T1 and T2⁎ in the quadriceps and patellar tendons of healthy volunteers at a field strength of 3 T and visualize the results based on 3D segmentation by using bivariate histogram analysis. We applied a 3D ultra-short echo-time imaging sequence with either variable repetition times (VTR) or variable flip angles (VFA) for T1 quantification in combination with multi-echo acquisition for extracting T2⁎. The values of both relaxation parameters were subsequently binned for bivariate histogram analysis and corresponding cluster identification, which were subsequently visualized. Based on manually-drawn regions of interest in the tendons on the relaxation parameter maps, T1 and T2⁎ boundaries were selected in the bivariate histogram to segment the quadriceps and patellar tendons and visualize the relaxation times by 3D volumetric rendering. Segmentation of bone marrow, fat, muscle and tendons was successfully performed based on the bivariate histogram analysis. Based on the segmentation results mean T2⁎ relaxation times, over the entire tendon volumes averaged over all subjects, were 1.8 ms ± 0.1 ms and 1.4 ms ± 0.2 ms for the patellar and quadriceps tendons, respectively. The mean T1 value of the patellar tendon, averaged over all subjects, was 527 ms ± 42 ms and 476 ms ± 40 ms for the VFA and VTR acquisitions, respectively. The quadriceps tendon had higher mean T1 values of 662 ms ± 97 ms (VFA method) and 637 ms ± 40 ms (VTR method) compared to the patellar tendon. 3D volumetric visualization of the relaxation times revealed that T1 values are not constant over the volume of both tendons, but vary locally. This work provided additional data to build upon the scarce literature available on relaxation times in the quadriceps and patellar tendons. We were able to segment both tendons and to visualize the relaxation parameter distributions over the entire tendon volumes. Y1 - 2019 U6 - https://doi.org/10.1016/j.mri.2019.07.015 VL - 63 IS - 11 SP - 29 EP - 36 ER - TY - GEN A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - An as-invariant-as-possible GL+(3)-based Statistical Shape Model N2 - We describe a novel nonlinear statistical shape model basedon differential coordinates viewed as elements of GL+(3). We adopt an as-invariant-as possible framework comprising a bi-invariant Lie group mean and a tangent principal component analysis based on a unique GL+(3)-left-invariant, O(3)-right-invariant metric. Contrary to earlier work that equips the coordinates with a specifically constructed group structure, our method employs the inherent geometric structure of the group-valued data and therefore features an improved statistical power in identifying shape differences. We demonstrate this in experiments on two anatomical datasets including comparison to the standard Euclidean as well as recent state-of-the-art nonlinear approaches to statistical shape modeling. T3 - ZIB-Report - 19-46 KW - Statistical shape analysis KW - Tangent principal component analysis KW - Lie groups KW - Classification KW - Manifold valued statistics Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-74566 SN - 1438-0064 ER - TY - CHAP A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - An as-invariant-as-possible GL+(3)-based Statistical Shape Model T2 - Proc. 7th MICCAI workshop on Mathematical Foundations of Computational Anatomy (MFCA) N2 - We describe a novel nonlinear statistical shape model basedon differential coordinates viewed as elements of GL+(3). We adopt an as-invariant-as possible framework comprising a bi-invariant Lie group mean and a tangent principal component analysis based on a unique GL+(3)-left-invariant, O(3)-right-invariant metric. Contrary to earlier work that equips the coordinates with a specifically constructed group structure, our method employs the inherent geometric structure of the group-valued data and therefore features an improved statistical power in identifying shape differences. We demonstrate this in experiments on two anatomical datasets including comparison to the standard Euclidean as well as recent state-of-the-art nonlinear approaches to statistical shape modeling. Y1 - 2019 U6 - https://doi.org/10.1007/978-3-030-33226-6_23 VL - 11846 SP - 219 EP - 228 PB - Springer ER - TY - GEN A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - A Surface-Theoretic Approach for Statistical Shape Modeling N2 - We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. We evaluate the performance of our model w.r.t. shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing natural biological shape variability, we carry out an analysis of specificity and generalization ability. T3 - ZIB-Report - 19-20 KW - Statistical shape analysis KW - Principal geodesic analysis KW - Lie groups KW - Classification KW - Manifold valued statistics Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-74497 SN - 1438-0064 ER - TY - CHAP A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - A Surface-Theoretic Approach for Statistical Shape Modeling T2 - Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part IV N2 - We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. We evaluate the performance of our model w.r.t. shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model’s ability of capturing natural biological shape variability, we carry out an analysis of specificity and generalization ability. Y1 - 2019 U6 - https://doi.org/10.1007/978-3-030-32251-9_3 VL - 11767 SP - 21 EP - 29 PB - Springer ER - TY - CHAP A1 - Estacio, Laura A1 - Ehlke, Moritz A1 - Tack, Alexander A1 - Castro-Gutierrez, Eveling A1 - Lamecker, Hans A1 - Mora, Rensso A1 - Zachow, Stefan T1 - Unsupervised Detection of Disturbances in 2D Radiographs T2 - 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) N2 - We present a method based on a generative model for detection of disturbances such as prosthesis, screws, zippers, and metals in 2D radiographs. The generative model is trained in an unsupervised fashion using clinical radiographs as well as simulated data, none of which contain disturbances. Our approach employs a latent space consistency loss which has the benefit of identifying similarities, and is enforced to reconstruct X-rays without disturbances. In order to detect images with disturbances, an anomaly score is computed also employing the Frechet distance between the input X-ray and the reconstructed one using our generative model. Validation was performed using clinical pelvis radiographs. We achieved an AUC of 0.77 and 0.83 with clinical and synthetic data, respectively. The results demonstrated a good accuracy of our method for detecting outliers as well as the advantage of utilizing synthetic data. Y1 - 2021 U6 - https://doi.org/10.1109/ISBI48211.2021.9434091 SP - 367 EP - 370 ER - TY - JOUR A1 - Sahu, Manish A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation JF - International Journal of Computer Assisted Radiology and Surgery N2 - Purpose Segmentation of surgical instruments in endoscopic video streams is essential for automated surgical scene understanding and process modeling. However, relying on fully supervised deep learning for this task is challenging because manual annotation occupies valuable time of the clinical experts. Methods We introduce a teacher–student learning approach that learns jointly from annotated simulation data and unlabeled real data to tackle the challenges in simulation-to-real unsupervised domain adaptation for endoscopic image segmentation. Results Empirical results on three datasets highlight the effectiveness of the proposed framework over current approaches for the endoscopic instrument segmentation task. Additionally, we provide analysis of major factors affecting the performance on all datasets to highlight the strengths and failure modes of our approach. Conclusions We show that our proposed approach can successfully exploit the unlabeled real endoscopic video frames and improve generalization performance over pure simulation-based training and the previous state-of-the-art. This takes us one step closer to effective segmentation of surgical instrument in the annotation scarce setting. Y1 - 2021 U6 - https://doi.org/10.1007/s11548-021-02383-4 N1 - Honorary Mention: Machine Learning for Computer-Assisted Intervention (CAI) Award @IPCAI2021 N1 - Honorary Mention: Audience Award for Best Innovation @IPCAI2021 VL - 16 SP - 849 EP - 859 PB - Springer Nature ER - TY - JOUR A1 - Tack, Alexander A1 - Preim, Bernhard A1 - Zachow, Stefan T1 - Fully automated Assessment of Knee Alignment from Full-Leg X-Rays employing a "YOLOv4 And Resnet Landmark regression Algorithm" (YARLA): Data from the Osteoarthritis Initiative JF - Computer Methods and Programs in Biomedicine N2 - We present a method for the quantification of knee alignment from full-leg X-Rays. A state-of-the-art object detector, YOLOv4, was trained to locate regions of interests (ROIs) in full-leg X-Ray images for the hip joint, the knee, and the ankle. Residual neural networks (ResNets) were trained to regress landmark coordinates for each ROI.Based on the detected landmarks the knee alignment, i.e., the hip-knee-ankle (HKA) angle, was computed. The accuracy of landmark detection was evaluated by a comparison to manually placed landmarks for 360 legs in 180 X-Rays. The accuracy of HKA angle computations was assessed on the basis of 2,943 X-Rays. Results of YARLA were compared to the results of two independent image reading studies(Cooke; Duryea) both publicly accessible via the Osteoarthritis Initiative. The agreement was evaluated using Spearman's Rho, and weighted kappa as well as regarding the correspondence of the class assignment (varus/neutral/valgus). The average difference between YARLA and manually placed landmarks was less than 2.0+- 1.5 mm for all structures (hip, knee, ankle). The average mismatch between HKA angle determinations of Cooke and Duryea was 0.09 +- 0.63°; YARLA resulted in a mismatch of 0.10 +- 0.74° compared to Cooke and of 0.18 +- 0.64° compared to Duryea. Cooke and Duryea agreed almost perfectly with respect to a weighted kappa value of 0.86, and showed an excellent reliability as measured by a Spearman's Rho value of 0.99. Similar values were achieved by YARLA, i.e., a weighted kappa value of0.83 and 0.87 and a Spearman's Rho value of 0.98 and 0.99 to Cooke and Duryea,respectively. Cooke and Duryea agreed in 92% of all class assignments and YARLA did so in 90% against Cooke and 92% against Duryea. In conclusion, YARLA achieved results comparable to those of human experts and thus provides a basis for an automated assessment of knee alignment in full-leg X-Rays. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1016/j.cmpb.2021.106080 VL - 205 IS - 106080 ER - TY - JOUR A1 - Sekuboyina, Anjany A1 - Bayat, Amirhossein A1 - Husseini, Malek E. A1 - Löffler, Maximilian 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 - Wei, Qingyue A1 - Brown, Kevin A1 - Wolf, Matthias A1 - Kirszenberg, Alexandre A1 - Puybareau, Élodie 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 - arXiv Y1 - 2020 ER - TY - JOUR A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - Rigid Motion Invariant Statistical Shape Modeling based on Discrete Fundamental Forms JF - Medical Image Analysis N2 - We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. Additionally, as planar configurations form a submanifold in shape space, our representation allows for effective estimation of quasi-isometric surfaces flattenings. We evaluate the performance of our model w.r.t. shape-based classification of hippocampus and femur malformations due to Alzheimer's disease and osteoarthritis, respectively. In particular, we achieve state-of-the-art accuracies outperforming the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing biological shape variability, we carry out an analysis of specificity and generalization ability. Y1 - 2021 U6 - https://doi.org/10.1016/j.media.2021.102178 VL - 73 ER - TY - JOUR A1 - Trepczynski, Adam A1 - Kneifel, Paul A1 - Heyland, Mark A1 - Leskovar, Marko A1 - Moewis, Philippe A1 - Damm, Philipp A1 - Taylor, William R. A1 - Zachow, Stefan A1 - Duda, Georg N. T1 - Impact of the external knee flexion moment on patello-femoral loading derived from in vivo loads and kinematics JF - Frontiers in Bioengineering and Biotechnology N2 - Introduction: Anterior knee pain and other patello-femoral (PF) complications frequently limit the success of total knee arthroplasty as the final treatment of end stage osteoarthritis. However, knowledge about the in-vivo loading conditions at the PF joint remains limited, as no direct measurements are available. We hypothesised that the external knee flexion moment (EFM) is highly predictive of the PF contact forces during activities with substantial flexion of the loaded knee. Materials and methods: Six patients (65–80 years, 67–101 kg) with total knee arthroplasty (TKA) performed two activities of daily living: sit-stand-sit and squat. Tibio-femoral (TF) contact forces were measured in vivo using instrumented tibial components, while synchronously internal TF and PF kinematics were captured with mobile fluoroscopy. The measurements were used to compute PF contact forces using patient specific musculoskeletal models. The relationship between the EFM and the PF contact force was quantified using linear regression. Results: Mean peak TF contact forces of 1.97–3.24 times body weight (BW) were found while peak PF forces reached 1.75 to 3.29 times body weight (BW). The peak EFM ranged from 3.2 to 5.9 %BW times body height, and was a good predictor of the PF contact force (R2 = 0.95 and 0.88 for sit-stand-sit and squat, respectively). Discussion: The novel combination of in vivo TF contact forces and internal patellar kinematics enabled a reliable assessment of PF contact forces. The results of the regression analysis suggest that PF forces can be estimated based solely on the EFM from quantitative gait analysis. Our study also demonstrates the relevance of PF contact forces, which reach magnitudes similar to TF forces during activities of daily living. Y1 - 2024 U6 - https://doi.org/10.3389/fbioe.2024.1473951 SN - 2296-4185 VL - 12/2024 PB - Frontiers Media SA ER - TY - JOUR A1 - Komnik, Igor A1 - Funken, Johannes A1 - Zachow, Stefan A1 - Schmidt-Wiethoff, Rüdiger A1 - Ellermann, Andree A1 - Potthast, Wolfgang T1 - Surgical planning in HTO – Alternative approaches to the Fujisawa gold-standard JF - Technology and Health Care N2 - BACKGROUND: Presurgical planning of the correction angle plays a decisive role in a high tibial osteotomy, affecting the loading situation in the knee affected by osteoarthritis. The planning approach by Fujisawa et al. aims to adjust the weight-bearing line to achieve an optimal knee joint load distribution. While this method is accessible, it may not fully consider the complexity of individual dynamic knee-loading profiles. This review aims to disclose existing alternative HTO planning methods that do not follow Fujisawa’s standard. METHODS: PubMed, Web of Science and CENTRAL databases were screened, focusing on HTO research in combination with alternative planning approaches. RESULTS: Eight out of 828 studies were included, with seven simulation studies based on finite element analysis and multi-body dynamics. The planning approaches incorporated gradual degrees of realignment parameters (weight-bearing line shift, medial proximal tibial angle, hip- knee-ankle, knee joint line orientation), simulating their effect on knee kinematics, contact force/stress, Von Mises and shear stress. Two studies proposed implementing individual correction magnitudes derived from preoperatively predicted knee adduction moments. CONCLUSION: Most planning methods depend on static alignment assessments, neglecting an adequate loading-depending profile. They are confined to their conceptual phases, making the associated planning methods unviable for current clinical use. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-98227 ER - TY - GEN A1 - Stalling, Detlev A1 - Seebass, Martin A1 - Zachow, Stefan T1 - Mehrschichtige Oberflächenmodelle zur computergestützten Planung in der Chirurgie N2 - Polygonale Schädelmodelle bilden ein wichtiges Hilfsmittel für computergestützte Planungen im Bereich der plastischen Chirurgie. Wir beschreiben, wie derartige Modelle automatisch aus hochaufgelösten CT-Datensätzen erzeugt werden können. Durch einen lokal steuerbaren Simplifizierungsalgorithmus werden die Modelle so weit vereinfacht, daß auch auf kleineren Graphikcomputern interaktives Arbeiten möglich wird. Die Verwendung eines speziellen Transparenzmodells ermöglicht den ungehinderten Blick auf die bei der Planung relevanten Knochenstrukturen und läßt den Benutzer zugleich die Kopfumrisse des Patienten erkennen. T3 - ZIB-Report - TR-98-05 KW - Isoflächen KW - Simplifizierung KW - Transparenzen Y1 - 1998 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-5661 ER - TY - JOUR A1 - Deuflhard, Peter A1 - Weiser, Martin A1 - Zachow, Stefan T1 - Mathematics in Facial Surgery JF - AMS Notices Y1 - 2006 VL - 53 IS - 9 SP - 1012 EP - 1016 ER - TY - JOUR A1 - Zinser, Max A1 - Zachow, Stefan A1 - Sailer, Hermann T1 - Bimaxillary "rotation advancement" procedures in patients with obstructive sleep apnea: A 3-dimensional airway analysis of morphological changes JF - International Journal of Oral & Maxillofacial Surgery Y1 - 2013 U6 - https://doi.org/10.1016/j.ijom.2012.08.002 VL - 42 IS - 5 SP - 569 EP - 578 ER - TY - GEN A1 - Deuflhard, Peter A1 - Zachow, Stefan T1 - Mathematische Therapie- und Operationsplanung Y1 - 2012 SP - 89 EP - 90 PB - Berliner Wirtschaftsgespräche e.V. CY - Berlin ER - TY - JOUR A1 - Galloway, Francis A1 - Kahnt, Max A1 - Ramm, Heiko A1 - Worsley, Peter A1 - Zachow, Stefan A1 - Nair, Prasanth A1 - Taylor, Mark T1 - A large scale finite element study of a cementless osseointegrated tibial tray JF - Journal of Biomechanics Y1 - 2013 U6 - https://doi.org//10.1016/j.jbiomech.2013.04.021 VL - 46 IS - 11 SP - 1900 EP - 1906 ER - TY - GEN A1 - Grewe, Carl Martin A1 - Lamecker, Hans A1 - Zachow, Stefan ED - Hermanussen, Michael T1 - Landmark-based Statistical Shape Analysis T2 - Auxology - Studying Human Growth and Development url Y1 - 2013 UR - http://www.schweizerbart.de/publications/detail/isbn/9783510652785 SP - 199 EP - 201 PB - Schweizerbart Verlag, Stuttgart ER - TY - JOUR A1 - Ehlke, Moritz A1 - Ramm, Heiko A1 - Lamecker, Hans A1 - Hege, Hans-Christian A1 - Zachow, Stefan T1 - Fast Generation of Virtual X-ray Images for Reconstruction of 3D Anatomy JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-35928 VL - 19 IS - 12 SP - 2673 EP - 2682 ER - TY - CHAP A1 - Hoffmann, René A1 - Zachow, Stefan T1 - Non-invasive approach to shed new light on the buoyancy business of chambered cephalopods (Mollusca) T2 - Proc. of the Intl. Assoc. for Mathematical Geosciences, Salzburg Y1 - 2011 U6 - https://doi.org/10.5242/iamg.2011.0163 SP - 1 EP - 11 ER - TY - GEN A1 - Grewe, Carl Martin A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Digital morphometry: The Potential of Statistical Shape Models T2 - Anthropologischer Anzeiger. Journal of Biological and Clinical Anthropology Y1 - 2011 SP - 506 EP - 506 ER - TY - CHAP A1 - Lamecker, Hans A1 - Kainmüller, Dagmar A1 - Seim, Heiko A1 - Zachow, Stefan T1 - Automatische 3D Rekonstruktion des Unterkiefers und der Mandibulärnerven auf Basis dentaler Bildgebung T2 - Proc. BMT, Biomed Tech Y1 - 2010 VL - 55 (Suppl. 1) SP - 35 EP - 36 PB - Walter de Gruyter-Verlag ER - TY - JOUR A1 - Dworzak, Jalda A1 - Lamecker, Hans A1 - von Berg, Jens A1 - Klinder, Tobias A1 - Lorenz, Cristian A1 - Kainmüller, Dagmar A1 - Seim, Heiko A1 - Hege, Hans-Christian A1 - Zachow, Stefan T1 - 3D Reconstruction of the Human Rib Cage from 2D Projection Images using a Statistical Shape Model JF - Int. J. Comput. Assist. Radiol. Surg. Y1 - 2010 U6 - https://doi.org/10.1007/s11548-009-0390-2 SN - 1861-6410 VL - 5 IS - 2 SP - 111 EP - 124 PB - Springer ER - TY - JOUR A1 - Rybak, Jürgen A1 - Kuß, Anja A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hege, Hans-Christian A1 - Lienhard, Matthias A1 - Singer, Jochen A1 - Neubert, Kerstin A1 - Menzel, Randolf T1 - The Digital Bee Brain: Integrating and Managing Neurons in a Common 3D Reference System JF - Front. Syst. Neurosci. Y1 - 2010 U6 - https://doi.org/10.3389/fnsys.2010.00030 VL - 4 IS - 30 ER - TY - CHAP A1 - von Berg, Jens A1 - Dworzak, Jalda A1 - Klinder, Tobias A1 - Manke, Dirk A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Lorenz, Cristian T1 - Temporal Subtraction of Chest Radiographs Compensating Pose Differences T2 - SPIE Medical Imaging Y1 - 2011 ER - TY - CHAP A1 - Bindernagel, Matthias A1 - Kainmüller, Dagmar A1 - Seim, Heiko A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hege, Hans-Christian T1 - An Articulated Statistical Shape Model of the Human Knee T2 - Bildverarbeitung für die Medizin 2011 Y1 - 2011 U6 - https://doi.org/10.1007/978-3-642-19335-4_14 SP - 59 EP - 63 PB - Springer ER - TY - CHAP A1 - Kahnt, Max A1 - Galloway, Francis A1 - Seim, Heiko A1 - Lamecker, Hans A1 - Taylor, Mark A1 - Zachow, Stefan T1 - Robust and Intuitive Meshing of Bone-Implant Compounds T2 - CURAC Y1 - 2011 SP - 71 EP - 74 CY - Magdeburg ER - TY - GEN A1 - SK, Saevarsson A1 - GB, Sharma A1 - S, Montgomery A1 - KCT, Ho A1 - Ramm, Heiko A1 - Lieck, Robert A1 - Zachow, Stefan A1 - C, Anglin T1 - Kinematic Comparison Between Gender Specific and Traditional Femoral Implants T2 - Proceedings of the 11th Alberta Biomedical Engineering (BME) Conference (Poster) Y1 - 2011 SP - 80 ER - TY - CHAP A1 - Deuflhard, Peter A1 - Dössel, Olaf A1 - Louis, Alfred A1 - Zachow, Stefan T1 - More Mathematics into Medicine! T2 - Production Factor Mathematics Y1 - 2010 UR - http://www.springer.com/mathematics/book/978-3-642-11247-8 SP - 357 EP - 378 PB - Springer ER - TY - CHAP A1 - Seim, Heiko A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Bindernagel, Matthias A1 - Malinowski, Jana A1 - Zachow, Stefan ED - v. Ginneken, B. T1 - Model-based Auto-Segmentation of Knee Bones and Cartilage in MRI Data T2 - Proc. MICCAI Workshop Medical Image Analysis for the Clinic Y1 - 2010 SP - 215 EP - 223 ER - TY - JOUR A1 - Weiser, Martin A1 - Zachow, Stefan A1 - Deuflhard, Peter T1 - Craniofacial Surgery Planning Based on Virtual Patient Models JF - it - Information Technology Y1 - 2010 U6 - https://doi.org/10.1524/itit.2010.0600 VL - 52 IS - 5 SP - 258 EP - 263 PB - Oldenbourg Verlagsgruppe ER - TY - CHAP A1 - Zachow, Stefan A1 - Hahn, Horst A1 - Lange, Thomas ED - Schlag, Peter ED - Eulenstein, Sebastian ED - Lange, Thomas T1 - Computerassistierte Chirugieplanung T2 - Computerassistierte Chirurgie Y1 - 2010 SP - 119 EP - 149 PB - Elsevier ER - TY - CHAP A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Seim, Heiko A1 - Zachow, Stefan A1 - Hege, Hans-Christian ED - Navab, Tianzi ED - P. W. Pluim, Josien ED - Viergever, Max T1 - Improving Deformable Surface Meshes through Omni-directional Displacements and MRFs T2 - Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI) Y1 - 2010 U6 - https://doi.org/10.1007/978-3-642-15705-9_28 VL - 6361 SP - 227 EP - 234 PB - Springer ER - TY - CHAP A1 - Zachow, Stefan A1 - Kubiack, Kim A1 - Malinowski, Jana A1 - Lamecker, Hans A1 - Essig, Harald A1 - Gellrich, Nils-Claudius T1 - Modellgestützte chirurgische Rekonstruktion komplexer Mittelgesichtsfrakturen T2 - Proc. BMT, Biomed Tech 2010 Y1 - 2010 VL - 55 (Suppl 1) SP - 107 EP - 108 PB - Walter de Gruyter-Verlag ER - TY - GEN A1 - Bindernagel, Matthias A1 - Kainmüller, Dagmar A1 - Ramm, Heiko A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Analysis of inter-individual anatomical shape variations of joint structures T2 - Proc. Int. Society of Computer Assisted Orthopaedic Surgery (CAOS) Y1 - 2012 IS - 210 ER - TY - CHAP A1 - Nguyen, The Duy A1 - Lamecker, Hans A1 - Kainmüller, Dagmar A1 - Zachow, Stefan ED - Ayache, Nicholas ED - Delingette, Hervé ED - Golland, Polina ED - Mori, Kensaku T1 - Automatic Detection and Classification of Teeth in CT Data T2 - Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI) Y1 - 2012 VL - 7510 SP - 609 EP - 616 ER - TY - JOUR A1 - Nguyen, The Duy A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Automatic bone and tooth detection for CT-based dental implant planning JF - Int. J. Computer Assisted Radiology and Surgery Y1 - 2012 VL - 7, Supplement 1 IS - 1 SP - 293 EP - 294 PB - Springer ER - TY - GEN A1 - Ehlke, Moritz A1 - Ramm, Heiko A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Efficient projection and deformation of volumetric shape and intensity models for accurate simulation of X-ray images T2 - Eurographics Workshop on Visual Computing for Biomedicine (NVIDIA best poster award) Y1 - 2012 ER - TY - JOUR A1 - Ramm, Heiko A1 - Kahnt, Max A1 - Zachow, Stefan T1 - Patientenspezifische Simulationsmodelle für die funktionelle Analyse von künstlichem Gelenkersatz JF - Computer Aided Medical Engineering (CaMe) Y1 - 2012 VL - 3 IS - 2 SP - 30 EP - 36 ER - TY - CHAP A1 - Kahnt, Max A1 - Ramm, Heiko A1 - Lamecker, Hans A1 - Zachow, Stefan ED - Levine, Joshua A. ED - Paulsen, Rasmus R. ED - Zhang, Yongjie T1 - Feature-Preserving, Multi-Material Mesh Generation using Hierarchical Oracles T2 - Proc. MICCAI Workshop on Mesh Processing in Medical Image Analysis (MeshMed) Y1 - 2012 VL - 7599 SP - 101 EP - 111 ER - TY - CHAP A1 - Ramm, Heiko A1 - Zachow, Stefan ED - Niederlag, Wolfgang ED - Lemke, Heinz ED - Peitgen, Heinz-Otto ED - Lehrach, Hans T1 - Computergestützte Planung für die individuelle Implantatversorgung T2 - Health Academy Y1 - 2012 VL - 16 SP - 145 EP - 158 ER - TY - CHAP A1 - Neugebauer, Mathias A1 - Janiga, Gabor A1 - Zachow, Stefan A1 - Krischek, Özlem A1 - Preim, Bernhard ED - Hauser, Helwig T1 - Generierung qualitativ hochwertiger Modelle für die Simulation von Blutfluss in zerebralen Aneurysmen T2 - Proc. of Simulation and Visualization 2008 Y1 - 2008 SP - 221 EP - 235 ER - TY - CHAP A1 - Dornheim, Jana A1 - Born, Silvia A1 - Zachow, Stefan A1 - Gessat, Michael A1 - Wellein, Daniela A1 - Strauß, Gero A1 - Preim, Bernhard A1 - Bartz, Dirk ED - Hauser, Helwig T1 - Bildanalyse, Visualisierung und Modellerstellung für die Implantatplanung im Mittelohr T2 - Proc. of Simulation and Visualization 2008 Y1 - 2008 SP - 139 EP - 154 ER - TY - JOUR A1 - Steinmann, Alexander A1 - Bartsch, Peter A1 - Zachow, Stefan A1 - Hildebrandt, Thomas T1 - Breathing Easily: Simulation of airflow in human noses can become a useful rhinosurgery planning tool JF - ANSYS Advantage Y1 - 2008 VL - Vol. II, No. 1 SP - 30 EP - 31 ER - TY - CHAP A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Seim, Heiko A1 - Zinser, Max A1 - Zachow, Stefan ED - Yang, Guang-Zhong ED - J. Hawkes, David ED - Rueckert, Daniel ED - Noble, J. Alison ED - J. Taylor, Chris T1 - Automatic Extraction of Mandibular Nerve and Bone from Cone-Beam CT Data T2 - Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI) Y1 - 2009 SP - 76 EP - 83 CY - London, UK ER - TY - CHAP A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hege, Hans-Christian T1 - An Articulated Statistical Shape Model for Accurate Hip Joint Segmentation T2 - EBMC 2009. Int. Conf. of the IEEE Eng. in Med. and Biol. Society (EMBC) Y1 - 2009 SP - 6345 EP - 6351 CY - Minneapolis, USA ER - TY - CHAP A1 - Seim, Heiko A1 - Kainmüller, Dagmar A1 - Heller, Markus O. A1 - Zachow, Stefan A1 - Hege, Hans-Christian T1 - Automatic Extraction of Anatomical Landmarks from Medical Image Data: An Evaluation of Different Methods T2 - Proc. of IEEE Int. Symposium on Biomedical Imaging (ISBI) Y1 - 2009 SP - 538 EP - 541 CY - Boston, MA, USA ER - TY - JOUR A1 - Zachow, Stefan A1 - Lamecker, Hans A1 - Zöckler, Maja A1 - Haberl, Ernst T1 - Computergestützte Planung zur chirurgischen Korrektur von frühkindlichen Schädelfehlbildungen (Craniosynostosen) JF - Face 02/09, Int. Mag. of Orofacial Esthetics, Oemus Journale Leipzig Y1 - 2009 SP - 48 EP - 53 ER - TY - JOUR A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Multi-object Segmentation with Coupled Deformable Models JF - Annals of the British Machine Vision Association (BMVA) Y1 - 2009 VL - 5 SP - 1 EP - 10 ER - TY - CHAP A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hege, Hans-Christian T1 - Coupling Deformable Models for Multi-object Segmentation T2 - Proc. Int. Symp. on Computational Models for Biomedical Simulation (ISBMS) Y1 - 2008 U6 - https://doi.org/10.1007/978-3-540-70521-5_8 SP - 69 EP - 78 ER - TY - CHAP A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Heller, Markus O. A1 - Hege, Hans-Christian T1 - Multi-Object Segmentation with Coupled Deformable Models T2 - Proc. Medical Image Understanding and Analysis Y1 - 2008 SP - 34 EP - 38 ER - TY - CHAP A1 - Seim, Heiko A1 - Kainmüller, Dagmar A1 - Kuss, Anja A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Menzel, Randolf A1 - Rybak, Juergen T1 - Model-based autosegmentation of the central brain of the honeybee, Apis mellifera, using active statistical shape models T2 - Proc. 1st INCF Congress of Neuroinformatics: Databasing and Modeling the Brain Y1 - 2008 U6 - https://doi.org/10.3389/conf.neuro.11.2008.01.064 ER - TY - CHAP A1 - Dworzak, Jalda A1 - Lamecker, Hans A1 - von Berg, Jens A1 - Klinder, Tobias A1 - Lorenz, Cristian A1 - Kainmüller, Dagmar A1 - Seim, Heiko A1 - Hege, Hans-Christian A1 - Zachow, Stefan T1 - Towards model-based 3-D reconstruction of the human rib cage from radiographs T2 - Proc. 7. Jahrestagung der Deutschen Gesellschaft für Computer-Roboterassistierte Chirurgie (CURAC) Y1 - 2008 SP - 193 EP - 196 ER - TY - CHAP A1 - Seim, Heiko A1 - Kainmüller, Dagmar A1 - Heller, Markus O. A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hege, Hans-Christian T1 - Automatic Segmentation of the Pelvic Bones from CT Data Based on a Statistical Shape Model T2 - Eurographics Workshop on Visual Computing for Biomedicine (VCBM) Y1 - 2008 SP - 93 EP - 100 CY - Delft, Netherlands ER -