TY - CHAP A1 - Ehlke, Moritz A1 - Heyland, Mark A1 - Märdian, Sven A1 - Duda, Georg A1 - Zachow, Stefan T1 - Assessing the relative positioning of an osteosynthesis plate to the patient-specific femoral shape from plain 2D radiographs T2 - Proceedings of the 15th Annual Meeting of CAOS-International (CAOS) N2 - We present a novel method to derive the surface distance of an osteosynthesis plate w.r.t. the patient­specific surface of the distal femur based on 2D X­ray images. Our goal is to study from clinical data, how the plate­to­bone distance affects bone healing. The patient­specific 3D shape of the femur is, however, seldom recorded for cases of femoral osteosynthesis since this typically requires Computed Tomography (CT), which comes at high cost and radiation dose. Our method instead utilizes two postoperative X­ray images to derive the femoral shape and thus can be applied on radiographs that are taken in clinical routine for follow­up. First, the implant geometry is used as a calibration object to relate the implant and the individual X­ray images spatially in a virtual X­ray setup. In a second step, the patient­specific femoral shape and pose are reconstructed in the virtual setup by fitting a deformable statistical shape and intensity model (SSIM) to the images. The relative positioning between femur and implant is then assessed in terms of displacement between the reconstructed 3D shape of the femur and the plate. A preliminary evaluation based on 4 cadaver datasets shows that the method derives the plate­to­bone distance with a mean absolute error of less than 1mm and a maximum error of 4.7 mm compared to ground truth from CT. We believe that the approach presented in this paper constitutes a meaningful tool to elucidate the effect of implant positioning on fracture healing. KW - 3d-­reconstruction from 2d X­rays KW - statistical shape and intensity models KW - fracture fixation of the distal femur KW - pose estimation Y1 - 2015 ER - TY - GEN A1 - Kober, Cornelia A1 - Sader, Robert A1 - Zeilhofer, Hans-Florian A1 - Prohaska, Steffen A1 - Zachow, Stefan A1 - Deuflhard, Peter T1 - Anisotrope Materialmodellierung für den menschlichen Unterkiefer N2 - Im Rahmen der biomechanischen Simulation knöcherner Organe ist die Frage nach einer befriedigenden Materialbeschreibung nach wie vor ungelöst. Computertomographische Datensätze liefern eine räumliche Verteilung der (Röntgen-)Dichte und ermöglichen damit eine gute Darstellung der individuellen Geometrie. Weiter können die verschiedenen Materialbestandteile des Knochens, Spongiosa und Kortikalis, voneinander getrennt werden. Aber die richtungsabängige Information der Materialanisotropie ist verloren. In dieser Arbeit wird ein Ansatz für eine anisotrope Materialbeschreibung vorgestellt, die es ermöglicht, den Einfluss der individuellen knöchernen Struktur auf das makroskopische Materialverhalten abzuschätzen. T3 - ZIB-Report - 01-31 KW - menschlicher Unterkiefer KW - Simulation mit der Methode der finiten Elemente KW - innerer Aufbau des Knochens KW - anisotrope Elastizität Y1 - 2001 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6574 ER - TY - JOUR A1 - Gladilin, Evgeny A1 - Zachow, Stefan A1 - Deuflhard, Peter A1 - Hege, Hans-Christian T1 - Anatomy- and physics-based facial animation for craniofacial surgery simulations JF - Med Biol Eng Comput. Y1 - 2004 U6 - https://doi.org/10.1007/BF02344627 VL - 42(2) SP - 167 EP - 170 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 - 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 - Siqueira Rodrigues, Lucas A1 - Nyakatura, John A1 - Zachow, Stefan A1 - Israel, Johann Habakuk T1 - An Immersive Virtual Paleontology Application T2 - 13th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2022 N2 - Virtual paleontology studies digital fossils through data analysis and visualization systems. The discipline is growing in relevance for the evident advantages of non-destructive imaging techniques over traditional paleontological methods, and it has made significant advancements during the last few decades. However, virtual paleontology still faces a number of technological challenges, amongst which are interaction shortcomings of image segmentation applications. Whereas automated segmentation methods are seldom applicable to fossil datasets, manual exploration of these specimens is extremely time-consuming as it impractically delves into three-dimensional data through two-dimensional visualization and interaction means. This paper presents an application that employs virtual reality and haptics to virtual paleontology in order to evolve its interaction paradigms and address some of its limitations. We provide a brief overview of the challenges faced by virtual paleontology practitioners, a description of our immersive virtual paleontology prototype, and the results of a heuristic evaluation of our design. Y1 - 2022 U6 - https://doi.org/10.1007/978-3-031-06249-0 SP - 478 EP - 481 ER - TY - JOUR A1 - von Tycowicz, Christoph A1 - Ambellan, Felix A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - An Efficient Riemannian Statistical Shape Model using Differential Coordinates JF - Medical Image Analysis 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-Euclidean structure. A key advantage of our framework is that statistics in a manifold shape space becomes 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 Euclidean approach in terms of shape-based classification of morphological disorders. Y1 - 2018 U6 - https://doi.org/10.1016/j.media.2017.09.004 VL - 43 IS - 1 SP - 1 EP - 9 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 - 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 - 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 -