@inproceedings{KainmuellerLameckerZachowetal.2009, author = {Kainm{\"u}ller, Dagmar and Lamecker, Hans and Zachow, Stefan and Hege, Hans-Christian}, title = {An Articulated Statistical Shape Model for Accurate Hip Joint Segmentation}, series = {EBMC 2009. Int. Conf. of the IEEE Eng. in Med. and Biol. Society (EMBC)}, booktitle = {EBMC 2009. Int. Conf. of the IEEE Eng. in Med. and Biol. Society (EMBC)}, address = {Minneapolis, USA}, pages = {6345 -- 6351}, year = {2009}, language = {en} } @article{HepptHildebrandtSteinmannetal.2007, author = {Heppt, Werner and Hildebrandt, Thomas and Steinmann, Alexander and Zachow, Stefan}, title = {Aesthetic and Function in Rhinoplasty}, series = {Springer Journal}, volume = {264 (Suppl 1), RL 126}, journal = {Springer Journal}, pages = {307}, year = {2007}, language = {en} } @inproceedings{ZachowErdmannHegeetal.2004, author = {Zachow, Stefan and Erdmann, Bodo and Hege, Hans-Christian and Deuflhard, Peter}, title = {Advances in 3D osteotomy planning with 3D soft tissue prediction}, series = {Proc. 2nd International Symposium on Computer Aided Surgery around the Head, Abstract}, booktitle = {Proc. 2nd International Symposium on Computer Aided Surgery around the Head, Abstract}, address = {Bern}, pages = {31}, year = {2004}, language = {en} } @article{SahuMukhopadhyaySzengeletal., author = {Sahu, Manish and Mukhopadhyay, Anirban and Szengel, Angelika and Zachow, Stefan}, title = {Addressing multi-label imbalance problem of Surgical Tool Detection using CNN}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {12}, journal = {International Journal of Computer Assisted Radiology and Surgery}, number = {6}, publisher = {Springer}, doi = {10.1007/s11548-017-1565-x}, pages = {1013 -- 1020}, abstract = {Purpose: A fully automated surgical tool detection framework is proposed for endoscopic video streams. State-of-the-art surgical tool detection methods rely on supervised one-vs-all or multi-class classification techniques, completely ignoring the co-occurrence relationship of the tools and the associated class imbalance. Methods: In this paper, we formulate tool detection as a multi-label classification task where tool co-occurrences are treated as separate classes. In addition, imbalance on tool co-occurrences is analyzed and stratification techniques are employed to address the imbalance during Convolutional Neural Network (CNN) training. Moreover, temporal smoothing is introduced as an online post-processing step to enhance run time prediction. Results: Quantitative analysis is performed on the M2CAI16 tool detection dataset to highlight the importance of stratification, temporal smoothing and the overall framework for tool detection. Conclusion: The analysis on tool imbalance, backed by the empirical results indicates the need and superiority of the proposed framework over state-of-the-art techniques.}, language = {en} } @inproceedings{ZilskeLameckerZachow2008, author = {Zilske, Michael and Lamecker, Hans and Zachow, Stefan}, title = {Adaptive Remeshing of Non-Manifold Surfaces}, series = {Eurographics 2008 Annex to the Conf. Proc.}, booktitle = {Eurographics 2008 Annex to the Conf. Proc.}, pages = {207 -- 211}, year = {2008}, language = {en} } @misc{ZilskeLameckerZachow, author = {Zilske, Michael and Lamecker, Hans and Zachow, Stefan}, title = {Adaptive Remeshing of Non-Manifold Surfaces}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-9445}, number = {07-01}, abstract = {We present a unified approach for consistent remeshing of arbitrary non-manifold triangle meshes with additional user-defined feature lines, which together form a feature skeleton. Our method is based on local operations only and produces meshes of high regularity and triangle quality while preserving the geometry as well as topology of the feature skeleton and the input mesh.}, language = {en} } @inproceedings{GladilinZachowDeuflhardetal.2002, author = {Gladilin, Evgeny and Zachow, Stefan and Deuflhard, Peter and Hege, Hans-Christian}, title = {Adaptive Nonlinear Elastic FEM for Realistic Prediction of Soft Tissue in Craniofacial Surgery Simulations}, series = {Proc. SPIE Medical Imaging 2002}, volume = {4681}, booktitle = {Proc. SPIE Medical Imaging 2002}, editor = {K. Mun, Seong}, address = {San Diego, USA}, doi = {10.1117/12.466906}, pages = {1 -- 8}, year = {2002}, language = {en} } @article{MoldenhauerWeiserZachow, author = {Moldenhauer, Marian and Weiser, Martin and Zachow, Stefan}, title = {Adaptive Algorithms for Optimal Hip Implant Positioning}, series = {PAMM}, volume = {17}, journal = {PAMM}, number = {1}, doi = {10.1002/pamm.201710071}, pages = {203 -- 204}, abstract = {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.}, language = {en} } @inproceedings{TackZachow, author = {Tack, Alexander and Zachow, Stefan}, title = {Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, series = {IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, booktitle = {IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, doi = {10.1109/ISBI.2019.8759201}, pages = {40 -- 43}, abstract = {Volumetry of cartilage of the knee is needed for knee osteoarthritis (KOA) assessment. It is typically performed manually in a tedious and subjective process. We developed a method for an automated, segmentation-based quantification of cartilage volume by employing 3D Convolutional Neural Networks (CNNs). CNNs were trained in a supervised manner using magnetic resonance imaging data and cartilage volumetry readings performed by clinical experts for 1378 subjects provided by the Osteoarthritis Initiative. It was shown that 3D CNNs are able to achieve volume measures comparable to the magnitude of variation between expert readings and the real in vivo situation. In the future, accurate automated cartilage volumetry might support both, diagnosis of KOA as well as longitudinal analysis of KOA progression.}, language = {en} } @misc{TackZachow, author = {Tack, Alexander and Zachow, Stefan}, title = {Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-71439}, abstract = {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.}, language = {en} } @inproceedings{SeimKainmuellerLameckeretal.2009, author = {Seim, Heiko and Kainm{\"u}ller, Dagmar and Lamecker, Hans and Zachow, Stefan}, title = {A System for Unsupervised Extraction of Orthopaedic Parameters from CT Data}, series = {GI Workshop Softwareassistenten - Computerunterst{\"u}tzung f{\"u}r die medizinische Diagnose und Therapieplanung}, booktitle = {GI Workshop Softwareassistenten - Computerunterst{\"u}tzung f{\"u}r die medizinische Diagnose und Therapieplanung}, address = {L{\"u}beck, Germany}, pages = {1328 -- 1337}, year = {2009}, language = {en} } @misc{AmbellanZachowvonTycowicz, author = {Ambellan, Felix and Zachow, Stefan and von Tycowicz, Christoph}, title = {A Surface-Theoretic Approach for Statistical Shape Modeling}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-74497}, abstract = {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.}, language = {en} } @inproceedings{AmbellanZachowvonTycowicz, author = {Ambellan, Felix and Zachow, Stefan and von Tycowicz, Christoph}, title = {A Surface-Theoretic Approach for Statistical Shape Modeling}, series = {Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part IV}, volume = {11767}, booktitle = {Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part IV}, publisher = {Springer}, doi = {10.1007/978-3-030-32251-9_3}, pages = {21 -- 29}, abstract = {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.}, language = {en} } @misc{TycowiczAmbellanMukhopadhyayetal., author = {Tycowicz, Christoph von and Ambellan, Felix and Mukhopadhyay, Anirban and Zachow, Stefan}, title = {A Riemannian Statistical Shape Model using Differential Coordinates}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-61175}, abstract = {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.}, language = {en} } @inproceedings{ZachowHierlErdmann2004, author = {Zachow, Stefan and Hierl, Thomas and Erdmann, Bodo}, title = {A quantitative evaluation of 3D soft tissue prediction in maxillofacial surgery planning}, series = {Proc. 3. Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboter-assistierte Chirurgie e.V.}, booktitle = {Proc. 3. Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboter-assistierte Chirurgie e.V.}, address = {M{\"u}nchen}, year = {2004}, language = {en} } @article{PichtLeCalveTomaselloetal., author = {Picht, Thomas and Le Calve, Maxime and Tomasello, Rosario and Fekonja, Lucius and Gholami, Mohammad Fardin and Bruhn, Matthias and Zwick, Carola and Rabe, J{\"u}rgen P. and M{\"u}ller-Birn, Claudia and Vajkoczy, Peter and Sauer, Igor M. and Zachow, Stefan and Nyakatura, John A. and Ribault, Patricia and Pulverm{\"u}ller, Friedemann}, title = {A note on neurosurgical resection and why we need to rethink cutting}, series = {Neurosurgery}, volume = {89}, journal = {Neurosurgery}, number = {5}, doi = {10.1093/neuros/nyab326}, pages = {289 -- 291}, language = {en} } @inproceedings{GladilinZachowDeuflhardetal.2002, author = {Gladilin, Evgeny and Zachow, Stefan and Deuflhard, Peter and Hege, Hans-Christian}, title = {A nonlinear soft tissue model for craniofacial surgery simulations}, series = {Proc. of Modeling and Simulation for Computer-aided Medicine and Surgery (MS4CMS}, booktitle = {Proc. of Modeling and Simulation for Computer-aided Medicine and Surgery (MS4CMS}, publisher = {INRIA}, address = {Paris, France}, year = {2002}, language = {en} } @article{GladilinZachowDeuflhardetal.2002, author = {Gladilin, Evgeny and Zachow, Stefan and Deuflhard, Peter and Hege, Hans-Christian}, title = {A nonlinear elastic soft tissue model for craniofacial surgery simulations}, series = {ESAIM, Proc.}, volume = {12}, journal = {ESAIM, Proc.}, doi = {10.1051/proc:2002011}, pages = {61 -- 66}, year = {2002}, language = {en} } @article{LemanisZachowFusseisetal., author = {Lemanis, Robert and Zachow, Stefan and Fusseis, Florian and Hoffmann, Ren{\´e}}, title = {A new approach using high-resolution computed tomography to test the buoyant properties of chambered cephalopod shells}, series = {Paleobiology}, volume = {41}, journal = {Paleobiology}, number = {2}, publisher = {Cambridge University Press}, address = {Cambridge}, doi = {10.1017/pab.2014.17}, pages = {313 -- 329}, abstract = {The chambered shell of modern cephalopods functions as a buoyancy apparatus, allowing the animal to enter the water column without expending a large amount of energy to overcome its own weight. Indeed, the chambered shell is largely considered a key adaptation that allowed the earliest cephalopods to leave the ocean floor and enter the water column. It has been argued by some, however, that the iconic chambered shell of Paleozoic and Mesozoic ammonoids did not provide a sufficiently buoyant force to compensate for the weight of the entire animal, thus restricting ammonoids to a largely benthic lifestyle reminiscent of some octopods. Here we develop a technique using high-resolution computed tomography to quantify the buoyant properties of chambered shells without reducing the shell to ideal spirals or eliminating inherent biological variability by using mathematical models that characterize past work in this area. This technique has been tested on Nautilus pompilius and is now extended to the extant deep-sea squid Spirula spirula and the Jurassic ammonite Cadoceras sp. hatchling. Cadoceras is found to have possessed near-neutral to positive buoyancy if hatched when the shell possessed between three and five chambers. However, we show that the animal could also overcome degrees of negative buoyancy through swimming, similar to the paralarvae of modern squids. These calculations challenge past inferences of benthic life habits based solely on calculations of negative buoyancy. The calculated buoyancy of Cadoceras supports the possibility of planktonic dispersal of ammonite hatchlings. This information is essential to understanding ammonoid ecology as well as biotic interactions and has implications for the interpretation of geochemical data gained from the isotopic analysis of the shell.}, language = {en} } @article{LameckerKamerWittmersetal.2007, author = {Lamecker, Hans and Kamer, Lukas and Wittmers, Antonia and Zachow, Stefan and Kaup, Thomas and Schramm, Alexander and Noser, Hansrudi and Hammer, Beat}, title = {A method for the three-dimensional statistical shape analysis of the bony orbit}, series = {Proc. Computer Aided Surgery around the Head}, journal = {Proc. Computer Aided Surgery around the Head}, pages = {94 -- 97}, year = {2007}, language = {en} } @article{GallowayKahntRammetal.2013, author = {Galloway, Francis and Kahnt, Max and Ramm, Heiko and Worsley, Peter and Zachow, Stefan and Nair, Prasanth and Taylor, Mark}, title = {A large scale finite element study of a cementless osseointegrated tibial tray}, series = {Journal of Biomechanics}, volume = {46}, journal = {Journal of Biomechanics}, number = {11}, doi = {/10.1016/j.jbiomech.2013.04.021}, pages = {1900 -- 1906}, year = {2013}, language = {en} } @misc{TackShestakovLuedkeetal., author = {Tack, Alexander and Shestakov, Alexey and L{\"u}dke, David and Zachow, Stefan}, title = {A deep multi-task learning method for detection of meniscal tears in MRI data from the Osteoarthritis Initiative database}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-84415}, abstract = {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.}, language = {en} } @article{TackShestakovLuedkeetal., author = {Tack, Alexander and Shestakov, Alexey and L{\"u}dke, David and Zachow, Stefan}, title = {A deep multi-task learning method for detection of meniscal tears in MRI data from the Osteoarthritis Initiative database}, series = {Frontiers in Bioengineering and Biotechnology, section Biomechanics}, journal = {Frontiers in Bioengineering and Biotechnology, section Biomechanics}, doi = {10.3389/fbioe.2021.747217}, pages = {28 -- 41}, abstract = {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.}, language = {en} } @inproceedings{JoachimskyMaIckingetal., author = {Joachimsky, Robert and Ma, Lihong and Icking, Christian and Zachow, Stefan}, title = {A Collision-Aware Articulated Statistical Shape Model of the Human Spine}, series = {Proc. of the 18th annual conference on Computer- and Robot-assisted Surgery (CURAC)}, booktitle = {Proc. of the 18th annual conference on Computer- and Robot-assisted Surgery (CURAC)}, pages = {58 -- 64}, abstract = {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).}, language = {en} } @inproceedings{GladilinZachowDeuflhardetal.2001, author = {Gladilin, Evgeny and Zachow, Stefan and Deuflhard, Peter and Hege, Hans-Christian}, title = {A Biomechanical Model for Soft Tissue Simulation in Craniofacial Surgery}, series = {Medical Imaging and Augmented Reality (MIAR)}, booktitle = {Medical Imaging and Augmented Reality (MIAR)}, address = {Hong Kong, China}, doi = {10.1109/MIAR.2001.930276}, pages = {137 -- 141}, year = {2001}, language = {en} } @inproceedings{HeinLuethZachowetal.1999, author = {Hein, Andreas and Lueth, Tim and Zachow, Stefan and Stien, Malte}, title = {A 2D Planning Sytem for Robot-Assisted Interventions}, series = {Computer Assisted Radiology and Surgery}, booktitle = {Computer Assisted Radiology and Surgery}, publisher = {Elsevier Science B.V.}, pages = {1049}, year = {1999}, language = {en} } @article{DworzakLameckervonBergetal.2010, author = {Dworzak, Jalda and Lamecker, Hans and von Berg, Jens and Klinder, Tobias and Lorenz, Cristian and Kainm{\"u}ller, Dagmar and Seim, Heiko and Hege, Hans-Christian and Zachow, Stefan}, title = {3D Reconstruction of the Human Rib Cage from 2D Projection Images using a Statistical Shape Model}, series = {Int. J. Comput. Assist. Radiol. Surg.}, volume = {5}, journal = {Int. J. Comput. Assist. Radiol. Surg.}, number = {2}, publisher = {Springer}, issn = {1861-6410}, doi = {10.1007/s11548-009-0390-2}, pages = {111 -- 124}, year = {2010}, language = {en} } @misc{ZachowZilskeHege, author = {Zachow, Stefan and Zilske, Michael and Hege, Hans-Christian}, title = {3D reconstruction of individual anatomy from medical image data: Segmentation and geometry processing}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-10440}, number = {07-41}, abstract = {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.}, language = {en} } @inproceedings{ZachowZilskeHege2007, author = {Zachow, Stefan and Zilske, Michael and Hege, Hans-Christian}, title = {3D Reconstruction of Individual Anatomy from Medical Image Data: Segmentation and Geometry Processing}, series = {25. ANSYS Conference \& CADFEM Users' Meeting}, booktitle = {25. ANSYS Conference \& CADFEM Users' Meeting}, address = {Dresden}, year = {2007}, language = {en} } @article{WestermarkZachowEppley2005, author = {Westermark, Anders and Zachow, Stefan and Eppley, Barry}, title = {3D osteotomy planning in maxillofacial surgery, including 3D soft tissue prediction}, series = {Journal of Craniofacial Surgery}, volume = {16(1)}, journal = {Journal of Craniofacial Surgery}, pages = {100 -- 104}, year = {2005}, language = {en} } @article{ZachowGladilinTrepczynskietal.2002, author = {Zachow, Stefan and Gladilin, Evgeny and Trepczynski, Adam and Sader, Robert and Zeilhofer, Hans-Florian}, title = {3D Osteotomy Planning in Cranio-Maxillofacial Surgery: Experiences and Results of Surgery Planning and Volumetric Finite-Element Soft Tissue Prediction in Three Clinical Cases}, series = {Computer Assisted Radiology and Surgery (CARS)}, journal = {Computer Assisted Radiology and Surgery (CARS)}, publisher = {Springer Verlag}, pages = {983 -- 987}, year = {2002}, language = {en} } @inproceedings{ZachowGladilinZeilhoferetal.2001, author = {Zachow, Stefan and Gladilin, Evgeny and Zeilhofer, Hans-Florian and Sader, Robert}, title = {3D Osteotomieplanung in der MKG-Chirurgie unter Ber{\"u}cksichtigung der r{\"a}umlichen Weichgewebeanordnung}, series = {Rechner- und sensorgest{\"u}tzte Chirurgie, GI Proc. zur SFB 414 Tagung}, booktitle = {Rechner- und sensorgest{\"u}tzte Chirurgie, GI Proc. zur SFB 414 Tagung}, address = {Heidelberg}, pages = {217 -- 226}, year = {2001}, language = {en} } @misc{EhlkeHeylandMaerdianetal., author = {Ehlke, Moritz and Heyland, Mark and M{\"a}rdian, Sven and Duda, Georg and Zachow, Stefan}, title = {3D Assessment of Osteosynthesis based on 2D Radiographs}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-56217}, abstract = {We present a novel method to derive the surface distance of an osteosynthesis plate w.r.t. the patient-specific surface of the distal femur based on postoperative 2D radiographs. In a first step, the implant geometry is used as a calibration object to relate the implant and the individual X-ray images spatially in a virtual X-ray setup. Second, the patient-specific femoral shape and pose are reconstructed by fitting a deformable statistical shape and intensity model (SSIM) to the X-rays. The relative positioning between femur and implant is then assessed in terms of the displacement between the reconstructed 3D shape of the femur and the plate. We believe that the approach presented in this paper constitutes a meaningful tool to elucidate the effect of implant positioning on fracture healing and, ultimately, to derive load recommendations after surgery.}, language = {en} } @inproceedings{EhlkeHeylandMaerdianetal., author = {Ehlke, Moritz and Heyland, Mark and M{\"a}rdian, Sven and Duda, Georg and Zachow, Stefan}, title = {3D Assessment of Osteosynthesis based on 2D Radiographs}, series = {Proceedings of the Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC)}, booktitle = {Proceedings of the Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC)}, pages = {317 -- 321}, abstract = {We present a novel method to derive the surface distance of an osteosynthesis plate w.r.t. the patient-specific surface of the distal femur based on postoperative 2D radiographs. In a first step, the implant geometry is used as a calibration object to relate the implant and the individual X-ray images spatially in a virtual X-ray setup. Second, the patient- specific femoral shape and pose are reconstructed by fitting a deformable statistical shape and intensity model (SSIM) to the X-rays. The relative positioning between femur and implant is then assessed in terms of the displacement between the reconstructed 3D shape of the femur and the plate. We believe that the approach presented in this paper constitutes a meaningful tool to elucidate the effect of implant positioning on fracture healing and, ultimately, to derive load recommendations after surgery.}, language = {en} }