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 - 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 - 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 - 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 - 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 - 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 - 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 - TY - JOUR A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Wittmers, Antonia A1 - Weber, Britta A1 - Hege, Hans-Christian A1 - Elsholtz, Barbara A1 - Stiller, Michael T1 - Automatic segmentation of mandibles in low-dose CT-data JF - Int. J. Computer Assisted Radiology and Surgery Y1 - 2006 VL - 1(1) SP - 393 EP - 395 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 N2 - We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging (MRI) 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 from 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 from the SKI10 challenge.For the first time, an accuracy equivalent to the inter-observer variability of human readers is 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 make 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 localized classification via CNNs with statistical anatomical knowledge via SSMs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data. T3 - ZIB-Report - 19-06 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-72704 SN - 1438-0064 N1 - Innovation Excellence Award 2020 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 - 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 - GEN 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 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. T3 - ZIB-Report - 15-21 KW - 3d-­reconstruction from 2d X­rays KW - statistical shape and intensity models KW - fracture fixation of the distal femur KW - pose estimation Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-54268 SN - 1438-0064 ER -