TY - GEN A1 - Joachimsky, Robert A1 - Ambellan, Felix A1 - Zachow, Stefan T1 - Computerassistierte Auswahl und Platzierung von interpositionalen Spacern zur Behandlung früher Gonarthrose 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. T3 - ZIB-Report - 17-72 KW - Osteoarthrose KW - Kniegelenk KW - Medizinische Planung KW - Visualisierung Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-66064 SN - 1438-0064 ER - TY - GEN A1 - Hege, Hans-Christian A1 - Merzky, Andre A1 - Zachow, Stefan T1 - Distributed Visualization with OpenGL Vizserver: Practical Experiences N2 - The increasing demand for distributed solutions in computing technology does not stop when it comes to visualization techniques. However, the capabilities of todays applications to perform remote rendering are limited by historical design legacys. Especially the popular X11 protokoll, which has been proven to be extremely flexible and usefull for remote 2D graphics applications, breaks down for the case of remote 3D rendering. In this white paper, we give a short overview of generic remote rendering technologies available today, and compare their performance to the recently released vizserver by SGI: a network extension to the SGI OpenGL rendering engines. T3 - ZIB-Report - 00-31 KW - remote visualization KW - high performance computing Y1 - 2000 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-5992 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 - THES A1 - Zachow, Stefan T1 - Computergestützte 3D Osteotomieplanung in der Mund-Kiefer-Gesichtschirurgie unter Berücksichtigung der räumlichen Weichgewebeanordnung T1 - Computer assisted 3D osteotomy planning in cranio-maxillofacial surgery under consideration of facial soft tissue arrangement N2 - In der Arbeit wird die computergestützte Planung von chirurgisch gesetzten Knochenfrakturen bzw. Knochenschnitten (sogenannten Osteotomien) an dreidimensionalen, computergrafischen Schädelmodellen, sowie die Umpositionierung separierter knöcherner Segmente im Kontext der rekonstruktiven MKG-Chirurgie behandelt. Durch die 3D Modellierung und Visualisierung anatomischer Strukturen, sowie der 3D Osteotomie- und Umstellungsplanung unter Einbeziehung der resultierenden Weichgewebedeformation wird den Chirurgen ein Werkzeug an die Hand gegeben, mit dem eine Therapieplanung am Computer durchgeführt und diese in Hinblick auf Funktion und Ästhetik bewertet werden kann. Unterschiedliche Strategien können dabei erprobt und in ihrer Auswirkung erfasst werden. Dazu wird ein methodischer Ansatz vorgestellt, der zum einen die chirurgische Planung im Vergleich zu existierenden Ansätzen deutlich verbessert und zum anderen eine robuste Weichgewebeprognose, durch den Einsatz geeigneter Planungsmodelle und eines physikalisch basierten Weichgewebemodells unter Nutzung numerischer Lösungsverfahren in die Planung integriert. Die Visualisierung der Planungsergebnisse erlaubt sowohl eine anschauliche und überzeugende, präoperative Patientenaufklärung, als auch die Demonstration möglicher Vorgehensweisen und deren Auswirkungen für die chirurgische Ausbildung. Ferner ergänzen die Planungsdaten die Falldokumentation und liefern einen Beitrag zur Qualitätssicherung. Die Arbeit ist in sieben Kapitel gegliedert und wie folgt strukturiert: Zuerst wird die medizinische Aufgabenstellung bei der chirurgischen Rekonstruktion von Knochenfehlbildungen und -fehlstellungen in der kraniofazialen Chirurgie sowie die daraus resultierenden Anforderungen an die Therapieplanung beschrieben. Anschließend folgt ein umfassender Überblick über entsprechende Vorarbeiten zur computergestützten Planung knochenverlagernder Operationen und eine kritische Bestandsaufnahme der noch vorhandenen Defizite. Nach der Vorstellung des eigenen Planungsansatzes wird die Generierung individueller, qualitativ hochwertiger 3D Planungsmodelle aus tomografischen Bilddaten beschrieben, die den Anforderungen an eine intuitive, 3D Planung von Umstellungsosteotomien entsprechen und eine Simulation der daraus resultierenden Weichgewebedeformation mittels der Finite-Elemente Methode (FEM) ermöglichen. Die Methoden der 3D Schnittplanung an computergrafischen Modellen werden analysiert und eine 3D Osteotomieplanung an polygonalen Schädelmodellen entwickelt, die es ermöglicht, intuitiv durch Definition von Schnittlinien am 3D Knochenmodell, eine den chirurgischen Anforderungen entsprechende Schnittplanung unter Berücksichtigung von Risikostrukturen durchzuführen. Separierte Knochensegmente lassen sich im Anschluss interaktiv umpositionieren und die resultierende Gesamtanordnung hinsichtlich einer funktionellen Rehabilitation bewerten. Aufgrund des in dieser Arbeit gewählten, physikalisch basierten Modellierungsansatzes kann unter Berücksichtigung des gesamten Weichgewebevolumens aus der Knochenverlagerung direkt die resultierende Gesichtsform berechnet werden. Dies wird anhand von 13 exemplarischen Fallstudien anschaulich demonstriert, wobei die Prognosequalität mittels postoperativer Fotografien und postoperativer CT-Daten überprüft und belegt wird. Die Arbeit wird mit einem Ausblick auf erweiterte Modellierungsansätze und einem Konzept für eine integrierte, klinisch einsetzbare Planungsumgebung abgeschlossen. N2 - In cranio-maxillofacial surgery, physicians are often faced with skeletal malformations that require complex bone relocations. Especially in severe cases of congenital dysgnathia (misalignment of upper and lower jaw) or hemifacial microsomia (asymmetric bone and tissue development), where multiple bone segments are to be mobilized and relocated simultaneously and in relation to each other, careful preoperative planning is mandatory. At present in clinical routine not all possible strategies can be planned and assessed with regard to functional rehabilitation. Moreover, the aesthetic outcome, i.e. the postoperative facial appearance, can only be estimated by a surgeon's experience and hardly communicated to the patient. On this account, a preoperative planning of complex osteotomies with bone relocations on a computerized model of a patient's head, including a reliable three-dimensional prediction and visualization of the post-surgical facial appearance is a highly appreciated possibility cranio-maxillofacial surgeons are longing for. This work, being performed at Zuse Institute Berlin (ZIB), addresses such a computer based 3D~surgery planning. A processing pipeline has been established and a simulation environment has been developed on basis of the software Amira, enabling a surgeon to perform bone cuts and bone rearrangements in an intuitive manner on virtual patient models. In addition, a prediction of the patients' postoperative appearance according to the relocated bone can be simulated and visualized realistically. For a meaningful planning of surgical procedures, anatomically correct patient models providing all relevant details are reconstructed from tomographic data with high fidelity. These patient models reliably represent bony structures as well as the facial soft tissue. Unstructured volumetric grids of the soft tissue are generated for a fast and efficient numerical solution of partial differential equations, describing tissue deformation on the foundation of 3D elastomechanics. The planning of osteotomies (bone cuts) for the mobilization and relocation of bone segments is performed in accordance to the planning on basis of life size replicas of a patient's skull, i.e. stereolitographic models. Osteotomy lines can be drawn on top of the polygonal planning models using suitable input devices. After evaluation of the consequence of a planned cut with regard to vulnerable inner structures (nerves, teeth etc.) the model is separated accordingly. A relocation of bone segments can be performed unrestrictedly in 3D or restricted to a translation or rotation within arbitrarily chosen planes under consideration of cephalometric guidelines. Bone and tooth collisions can be evaluated for functional analysis or orthodontic treatment planning with possible integration of digitized dental plaster casts. As a result of the preoperative planning, a single transformation matrix, encoding translation and rotation, or a sequence of such matrices are provided for each bone segment. Both the osteotomy paths and the transformation parameters can finally be used for intra-operative navigation. In the course of the planning, the relocated positions of bone segments serve as an input for the simulation of the resulting soft tissue deformation. Since bone and surrounding soft tissue share common boundaries that are either fixed or translocated, the resulting configuration of the entire tissue volume can be computed from the given boundary displacements by numerical minimization of the internal strain energy on basis of a biomechanical model, using a finite-element approach. In collaboration with different surgeons and hospitals more than 25 treatments have been accompanied by preoperative planning so far ranging from mandibular and midfacial hypoplasia to complex hemifacial microsomia. 13 of these cases are presented within this work. Simulation results were validated on the basis of photographs as well as of postoperative CT data, showing a good correlation between simulation and postoperative outcome. Further aspects of improving the modeling approach are discussed. It has been demonstrated that 3D~osteotomy planning on virtual patient models can be performed intuitively, and that 3D~tissue deformation for cranio-maxillofacial osteotomy planning can be predicted numerically without using heuristic ratios. It can be stated that by using 3D~planning software, a surgeon gains a better spatial understanding of complex dysplasia, and the 3D~soft tissue prediction gives an additional criterion for the assessment of the planned strategy. It turned out that, especially in complex cases such as hemifacial microsomia or for decisions bet­ween mono- and bimaxillary advancements, a 3D~planning aid is extremely helpful. The conclusion is, that images and animations created within the planning phase provide a valuable planning criterion for maxillofacial surgeons as well as a demonstrative information for patients and their relatives, thus greatly enhancing patient information, as well as surgical education. All data that result from the planning are also important for documentation and quality assurance. 3D osteotomy planning, including soft tissue prediction, likely will become a new paradigm of plastic and reconstructive surgery planning in the future. An assortment of results can be found under: http://www.zib.de/visual/medical/projects KW - MKG-Chirurgie KW - Mund-Kiefer-Gesichtschirurgie KW - Therapieplanung KW - Osteotomie KW - Weichgewebeprädiktion KW - computer assisted surgery KW - therapy planning KW - osteotomy KW - soft tissue prediction KW - CAS Y1 - 2005 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-10432 SN - 3899631986 ER - TY - GEN A1 - Zachow, Stefan A1 - Zilske, Michael A1 - Hege, Hans-Christian T1 - 3D reconstruction of individual anatomy from medical image data: Segmentation and geometry processing N2 - For medical diagnosis, visualization, and model-based therapy planning three-dimensional geometric reconstructions of individual anatomical structures are often indispensable. Computer-assisted, model-based planning procedures typically cover specific modifications of “virtual anatomy” as well as numeric simulations of associated phenomena, like e.g. mechanical loads, fluid dynamics, or diffusion processes, in order to evaluate a potential therapeutic outcome. Since internal anatomical structures cannot be measured optically or mechanically in vivo, three-dimensional reconstruction of tomographic image data remains the method of choice. In this work the process chain of individual anatomy reconstruction is described which consists of segmentation of medical image data, geometrical reconstruction of all relevant tissue interfaces, up to the generation of geometric approximations (boundary surfaces and volumetric meshes) of three-dimensional anatomy being suited for finite element analysis. All results presented herein are generated with amira ® – a highly interactive software system for 3D data analysis, visualization and geometry reconstruction. T3 - ZIB-Report - 07-41 KW - Medical image segmentation KW - computational geometry KW - virtual anatomy KW - finite element meshes Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-10440 SN - 1438-0064 ER - TY - GEN A1 - Grewe, C. Martin A1 - Zachow, Stefan T1 - Release of the FexMM for the Open Virtual Mirror Framework N2 - THIS MODEL IS FOR NON-COMMERCIAL RESEARCH PURPOSES. ONLY MEMBERS OF UNIVERSITIES OR NON-COMMERCIAL RESEARCH INSTITUTES ARE ELIGIBLE TO APPLY. 1. Download, fill, and sign the form available from: https://media.githubusercontent.com/media/mgrewe/ovmf/main/data/fexmm_license_agreement.pdf 2. Send the signed form to: fexmm@zib.de NOTE: Use an official email address of your institution for the request. Y1 - 2021 U6 - https://doi.org/10.12752/8532 ER - TY - JOUR A1 - Li, Jianning A1 - Pimentel, Pedro A1 - Szengel, Angelika A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Estacio, Laura A1 - Doenitz, Christian A1 - Ramm, Heiko A1 - Shi, Haochen A1 - Chen, Xiaojun A1 - Matzkin, Franco A1 - Newcombe, Virginia A1 - Ferrante, Enzo A1 - Jin, Yuan A1 - Ellis, David G. A1 - Aizenberg, Michele R. A1 - Kodym, Oldrich A1 - Spanel, Michal A1 - Herout, Adam A1 - Mainprize, James G. A1 - Fishman, Zachary A1 - Hardisty, Michael R. A1 - Bayat, Amirhossein A1 - Shit, Suprosanna A1 - Wang, Bomin A1 - Liu, Zhi A1 - Eder, Matthias A1 - Pepe, Antonio A1 - Gsaxner, Christina A1 - Alves, Victor A1 - Zefferer, Ulrike A1 - von Campe, Cord A1 - Pistracher, Karin A1 - Schäfer, Ute A1 - Schmalstieg, Dieter A1 - Menze, Bjoern H. A1 - Glocker, Ben A1 - Egger, Jan T1 - AutoImplant 2020 - First MICCAI Challenge on Automatic Cranial Implant Design JF - IEEE Transactions on Medical Imaging N2 - The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. Y1 - 2021 U6 - https://doi.org/10.1109/TMI.2021.3077047 SN - 0278-0062 VL - 40 IS - 9 SP - 2329 EP - 2342 ER - TY - JOUR A1 - Tack, Alexander A1 - Ambellan, Felix A1 - Zachow, Stefan T1 - Towards novel osteoarthritis biomarkers: Multi-criteria evaluation of 46,996 segmented knee MRI data from the Osteoarthritis Initiative JF - PLOS One N2 - Convolutional neural networks (CNNs) are the state-of-the-art for automated assessment of knee osteoarthritis (KOA) from medical image data. However, these methods lack interpretability, mainly focus on image texture, and cannot completely grasp the analyzed anatomies’ shapes. In this study we assess the informative value of quantitative features derived from segmentations in order to assess their potential as an alternative or extension to CNN-based approaches regarding multiple aspects of KOA. Six anatomical structures around the knee (femoral and tibial bones, femoral and tibial cartilages, and both menisci) are segmented in 46,996 MRI scans. Based on these segmentations, quantitative features are computed, i.e., measurements such as cartilage volume, meniscal extrusion and tibial coverage, as well as geometric features based on a statistical shape encoding of the anatomies. The feature quality is assessed by investigating their association to the Kellgren-Lawrence grade (KLG), joint space narrowing (JSN), incident KOA, and total knee replacement (TKR). Using gold standard labels from the Osteoarthritis Initiative database the balanced accuracy (BA), the area under the Receiver Operating Characteristic curve (AUC), and weighted kappa statistics are evaluated. Features based on shape encodings of femur, tibia, and menisci plus the performed measurements showed most potential as KOA biomarkers. Differentiation between non-arthritic and severely arthritic knees yielded BAs of up to 99%, 84% were achieved for diagnosis of early KOA. Weighted kappa values of 0.73, 0.72, and 0.78 were achieved for classification of the grade of medial JSN, lateral JSN, and KLG, respectively. The AUC was 0.61 and 0.76 for prediction of incident KOA and TKR within one year, respectively. Quantitative features from automated segmentations provide novel biomarkers for KLG and JSN classification and show potential for incident KOA and TKR prediction. The validity of these features should be further evaluated, especially as extensions of CNN- based approaches. To foster such developments we make all segmentations publicly available together with this publication. Y1 - 2021 U6 - https://doi.org/10.1371/journal.pone.0258855 VL - 16 IS - 10 ER - TY - JOUR A1 - Tack, Alexander A1 - Shestakov, Alexey A1 - Lüdke, David A1 - Zachow, Stefan T1 - A deep multi-task learning method for detection of meniscal tears in MRI data from the Osteoarthritis Initiative database JF - Frontiers in Bioengineering and Biotechnology, section Biomechanics N2 - We present a novel and computationally efficient method for the detection of meniscal tears in Magnetic Resonance Imaging (MRI) data. Our method is based on a Convolutional Neural Network (CNN) that operates on a complete 3D MRI scan. Our approach detects the presence of meniscal tears in three anatomical sub-regions (anterior horn, meniscal body, posterior horn) for both the Medial Meniscus (MM) and the Lateral Meniscus (LM) individually. For optimal performance of our method, we investigate how to preprocess the MRI data or how to train the CNN such that only relevant information within a Region of Interest (RoI) of the data volume is taken into account for meniscal tear detection. We propose meniscal tear detection combined with a bounding box regressor in a multi-task deep learning framework to let the CNN implicitly consider the corresponding RoIs of the menisci. We evaluate the accuracy of our CNN-based meniscal tear detection approach on 2,399 Double Echo Steady-State (DESS) MRI scans from the Osteoarthritis Initiative database. In addition, to show that our method is capable of generalizing to other MRI sequences, we also adapt our model to Intermediate-Weighted Turbo Spin-Echo (IW TSE) MRI scans. To judge the quality of our approaches, Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values are evaluated for both MRI sequences. For the detection of tears in DESS MRI, our method reaches AUC values of 0.94, 0.93, 0.93 (anterior horn, body, posterior horn) in MM and 0.96, 0.94, 0.91 in LM. For the detection of tears in IW TSE MRI data, our method yields AUC values of 0.84, 0.88, 0.86 in MM and 0.95, 0.91, 0.90 in LM. In conclusion, the presented method achieves high accuracy for detecting meniscal tears in both DESS and IW TSE MRI data. Furthermore, our method can be easily trained and applied to other MRI sequences. Y1 - 2021 U6 - https://doi.org/10.3389/fbioe.2021.747217 SP - 28 EP - 41 ER - TY - GEN A1 - Tack, Alexander A1 - Shestakov, Alexey A1 - Lüdke, David A1 - Zachow, Stefan T1 - A deep multi-task learning method for detection of meniscal tears in MRI data from the Osteoarthritis Initiative database N2 - We present a novel and computationally efficient method for the detection of meniscal tears in Magnetic Resonance Imaging (MRI) data. Our method is based on a Convolutional Neural Network (CNN) that operates on a complete 3D MRI scan. Our approach detects the presence of meniscal tears in three anatomical sub-regions (anterior horn, meniscal body, posterior horn) for both the Medial Meniscus (MM) and the Lateral Meniscus (LM) individually. For optimal performance of our method, we investigate how to preprocess the MRI data or how to train the CNN such that only relevant information within a Region of Interest (RoI) of the data volume is taken into account for meniscal tear detection. We propose meniscal tear detection combined with a bounding box regressor in a multi-task deep learning framework to let the CNN implicitly consider the corresponding RoIs of the menisci. We evaluate the accuracy of our CNN-based meniscal tear detection approach on 2,399 Double Echo Steady-State (DESS) MRI scans from the Osteoarthritis Initiative database. In addition, to show that our method is capable of generalizing to other MRI sequences, we also adapt our model to Intermediate-Weighted Turbo Spin-Echo (IW TSE) MRI scans. To judge the quality of our approaches, Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values are evaluated for both MRI sequences. For the detection of tears in DESS MRI, our method reaches AUC values of 0.94, 0.93, 0.93 (anterior horn, body, posterior horn) in MM and 0.96, 0.94, 0.91 in LM. For the detection of tears in IW TSE MRI data, our method yields AUC values of 0.84, 0.88, 0.86 in MM and 0.95, 0.91, 0.90 in LM. In conclusion, the presented method achieves high accuracy for detecting meniscal tears in both DESS and IW TSE MRI data. Furthermore, our method can be easily trained and applied to other MRI sequences. T3 - ZIB-Report - 21-33 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-84415 SN - 1438-0064 ER -