@article{BernardSalamancaThunbergetal.2016, author = {Bernard, Florian and Salamanca, Luis and Thunberg, Johan and Tack, Alexander and Jentsch, Dennis and Lamecker, Hans and Zachow, Stefan and Hertel, Frank and Goncalves, Jorge and Gemmar, Peter}, title = {Shape-aware Surface Reconstruction from Sparse Data}, journal = {arXiv}, arxiv = {http://arxiv.org/abs/arXiv:1602.08425v1}, pages = {1602.08425v1}, year = {2016}, abstract = {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.}, language = {en} } @article{BernardSalamancaThunbergetal.2017, author = {Bernard, Florian and Salamanca, Luis and Thunberg, Johan and Tack, Alexander and Jentsch, Dennis and Lamecker, Hans and Zachow, Stefan and Hertel, Frank and Goncalves, Jorge and Gemmar, Peter}, title = {Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds}, volume = {38}, journal = {Medical Image Analysis}, doi = {10.1016/j.media.2017.02.005}, pages = {77 -- 89}, year = {2017}, abstract = {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.}, language = {en} } @inproceedings{RammVictoriaMorilloTodtetal.2013, author = {Ramm, Heiko and Victoria Morillo, Oscar Salvador and Todt, Ingo and Schirmacher, Hartmut and Ernst, Arneborg and Zachow, Stefan and Lamecker, Hans}, title = {Visual Support for Positioning Hearing Implants}, booktitle = {Proceedings of the 12th annual meeting of the CURAC society}, editor = {Freysinger, Wolfgang}, pages = {116 -- 120}, year = {2013}, language = {en} } @inproceedings{AmbellanTackWilsonetal.2017, author = {Ambellan, Felix and Tack, Alexander and Wilson, Dave and Anglin, Carolyn and Lamecker, Hans and Zachow, Stefan}, title = {Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data}, volume = {16}, booktitle = {Proceedings of the Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC)}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-65339}, pages = {24 -- 30}, year = {2017}, abstract = {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.}, language = {en} } @misc{AmbellanTackWilsonetal.2017, author = {Ambellan, Felix and Tack, Alexander and Wilson, Dave and Anglin, Carolyn and Lamecker, Hans and Zachow, Stefan}, title = {Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-66052}, year = {2017}, abstract = {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.}, language = {en} } @article{BrueningHildebrandtHepptetal.2020, author = {Br{\"u}ning, Jan and Hildebrandt, Thomas and Heppt, Werner and Schmidt, Nora and Lamecker, Hans and Szengel, Angelika and Amiridze, Natalja and Ramm, Heiko and Bindernagel, Matthias and Zachow, Stefan and Goubergrits, Leonid}, title = {Characterization of the Airflow within an Average Geometry of the Healthy Human Nasal Cavity}, volume = {3755}, journal = {Scientific Reports}, number = {10}, doi = {10.1038/s41598-020-60755-3}, year = {2020}, abstract = {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.}, language = {en} } @article{PimentelSzengelEhlkeetal.2020, author = {Pimentel, Pedro and Szengel, Angelika and Ehlke, Moritz and Lamecker, Hans and Zachow, Stefan and Estacio, Laura and Doenitz, Christian and Ramm, Heiko}, title = {Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks}, volume = {12439}, journal = {Towards the Automatization of Cranial Implant Design in Cranioplasty}, editor = {Li, Jianning and Egger, Jan}, edition = {1}, publisher = {Springer International Publishing}, doi = {10.1007/978-3-030-64327-0_3}, pages = {16 -- 27}, year = {2020}, abstract = {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.}, language = {en} } @inproceedings{EstacioEhlkeTacketal.2021, author = {Estacio, Laura and Ehlke, Moritz and Tack, Alexander and Castro-Gutierrez, Eveling and Lamecker, Hans and Mora, Rensso and Zachow, Stefan}, title = {Unsupervised Detection of Disturbances in 2D Radiographs}, booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)}, doi = {10.1109/ISBI48211.2021.9434091}, pages = {367 -- 370}, year = {2021}, abstract = {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.}, language = {en} } @article{SekuboyinaBayatHusseinietal.2020, author = {Sekuboyina, Anjany and Bayat, Amirhossein and Husseini, Malek E. and L{\"o}ffler, Maximilian and Li, Hongwei and Tetteh, Giles and Kukačka, Jan and Payer, Christian and Štern, Darko and Urschler, Martin and Chen, Maodong and Cheng, Dalong and Lessmann, Nikolas and Hu, Yujin and Wang, Tianfu and Yang, Dong and Xu, Daguang and Ambellan, Felix and Amiranashvili, Tamaz and Ehlke, Moritz and Lamecker, Hans and Lehnert, Sebastian and Lirio, Marilia and de Olaguer, Nicol{\´a}s P{\´e}rez and Ramm, Heiko and Sahu, Manish and Tack, Alexander and Zachow, Stefan and Jiang, Tao and Ma, Xinjun and Angerman, Christoph and Wang, Xin and Wei, Qingyue and Brown, Kevin and Wolf, Matthias and Kirszenberg, Alexandre and Puybareau, {\´E}lodie and Valentinitsch, Alexander and Rempfler, Markus and Menze, Bj{\"o}rn H. and Kirschke, Jan S.}, title = {VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images}, journal = {arXiv}, arxiv = {http://arxiv.org/abs/2001.09193}, year = {2020}, language = {en} } @misc{LameckerLangeSeebass2004, author = {Lamecker, Hans and Lange, Thomas and Seebass, Martin}, title = {Segmentation of the Liver using a 3D Statistical Shape Model}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-7847}, number = {04-09}, year = {2004}, abstract = {This paper presents an automatic approach for segmentation of the liver from computer tomography (CT) images based on a 3D statistical shape model. Segmentation of the liver is an important prerequisite in liver surgery planning. One of the major challenges in building a 3D shape model from a training set of segmented instances of an object is the determination of the correspondence between different surfaces. We propose to use a geometric approach that is based on minimizing the distortion of the correspondence mapping between two different surfaces. For the adaption of the shape model to the image data a profile model based on the grey value appearance of the liver and its surrounding tissues in contrast enhanced CT data was developed. The robustness of this method results from a previous nonlinear diffusion filtering of the image data. Special focus is turned to the quantitative evaluation of the segmentation process. Several different error measures are discussed and implemented in a study involving more than 30 livers.}, language = {en} } @misc{GreweLameckerZachow2013, author = {Grewe, Carl Martin and Lamecker, Hans and Zachow, Stefan}, title = {Landmark-based Statistical Shape Analysis}, journal = {Auxology - Studying Human Growth and Development url}, editor = {Hermanussen, Michael}, publisher = {Schweizerbart Verlag, Stuttgart}, pages = {199 -- 201}, year = {2013}, language = {en} } @misc{GreweLameckerZachow2011, author = {Grewe, Carl Martin and Lamecker, Hans and Zachow, Stefan}, title = {Digital morphometry: The Potential of Statistical Shape Models}, journal = {Anthropologischer Anzeiger. Journal of Biological and Clinical Anthropology}, pages = {506 -- 506}, year = {2011}, language = {en} } @inproceedings{LameckerKainmuellerSeimetal.2010, author = {Lamecker, Hans and Kainm{\"u}ller, Dagmar and Seim, Heiko and Zachow, Stefan}, title = {Automatische 3D Rekonstruktion des Unterkiefers und der Mandibul{\"a}rnerven auf Basis dentaler Bildgebung}, volume = {55 (Suppl. 1)}, booktitle = {Proc. BMT, Biomed Tech}, publisher = {Walter de Gruyter-Verlag}, pages = {35 -- 36}, year = {2010}, language = {en} } @inproceedings{vonBergDworzakKlinderetal.2011, author = {von Berg, Jens and Dworzak, Jalda and Klinder, Tobias and Manke, Dirk and Lamecker, Hans and Zachow, Stefan and Lorenz, Cristian}, title = {Temporal Subtraction of Chest Radiographs Compensating Pose Differences}, booktitle = {SPIE Medical Imaging}, year = {2011}, language = {en} } @inproceedings{KahntGallowaySeimetal.2011, author = {Kahnt, Max and Galloway, Francis and Seim, Heiko and Lamecker, Hans and Taylor, Mark and Zachow, Stefan}, title = {Robust and Intuitive Meshing of Bone-Implant Compounds}, booktitle = {CURAC}, address = {Magdeburg}, pages = {71 -- 74}, year = {2011}, language = {en} } @article{LameckerPennec2010, author = {Lamecker, Hans and Pennec, Xavier}, title = {Atlas to Image-with-Tumor Registration based on Demons and Deformation Inpainting}, journal = {Proc. MICCAI Workshop on Computational Imaging Biomarkers for Tumors - From Qualitative to Quantitative (CIBT'2010)}, address = {Beijing, China}, year = {2010}, language = {en} } @inproceedings{SeimKainmuellerLameckeretal.2010, author = {Seim, Heiko and Kainm{\"u}ller, Dagmar and Lamecker, Hans and Bindernagel, Matthias and Malinowski, Jana and Zachow, Stefan}, title = {Model-based Auto-Segmentation of Knee Bones and Cartilage in MRI Data}, booktitle = {Proc. MICCAI Workshop Medical Image Analysis for the Clinic}, editor = {v. Ginneken, B.}, pages = {215 -- 223}, year = {2010}, language = {en} } @inproceedings{ZachowKubiackMalinowskietal.2010, author = {Zachow, Stefan and Kubiack, Kim and Malinowski, Jana and Lamecker, Hans and Essig, Harald and Gellrich, Nils-Claudius}, title = {Modellgest{\"u}tzte chirurgische Rekonstruktion komplexer Mittelgesichtsfrakturen}, volume = {55 (Suppl 1)}, booktitle = {Proc. BMT, Biomed Tech 2010}, publisher = {Walter de Gruyter-Verlag}, pages = {107 -- 108}, year = {2010}, language = {de} } @misc{BindernagelKainmuellerRammetal.2012, author = {Bindernagel, Matthias and Kainm{\"u}ller, Dagmar and Ramm, Heiko and Lamecker, Hans and Zachow, Stefan}, title = {Analysis of inter-individual anatomical shape variations of joint structures}, journal = {Proc. Int. Society of Computer Assisted Orthopaedic Surgery (CAOS)}, number = {210}, year = {2012}, language = {en} } @inproceedings{NguyenLameckerKainmuelleretal.2012, author = {Nguyen, The Duy and Lamecker, Hans and Kainm{\"u}ller, Dagmar and Zachow, Stefan}, title = {Automatic Detection and Classification of Teeth in CT Data}, volume = {7510}, booktitle = {Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI)}, editor = {Ayache, Nicholas and Delingette, Herv{\´e} and Golland, Polina and Mori, Kensaku}, pages = {609 -- 616}, year = {2012}, language = {en} } @article{NguyenKainmuellerLameckeretal.2012, author = {Nguyen, The Duy and Kainm{\"u}ller, Dagmar and Lamecker, Hans and Zachow, Stefan}, title = {Automatic bone and tooth detection for CT-based dental implant planning}, volume = {7, Supplement 1}, journal = {Int. J. Computer Assisted Radiology and Surgery}, number = {1}, publisher = {Springer}, pages = {293 -- 294}, year = {2012}, language = {en} } @misc{EhlkeRammLameckeretal.2012, author = {Ehlke, Moritz and Ramm, Heiko and Lamecker, Hans and Zachow, Stefan}, title = {Efficient projection and deformation of volumetric shape and intensity models for accurate simulation of X-ray images}, journal = {Eurographics Workshop on Visual Computing for Biomedicine (NVIDIA best poster award)}, year = {2012}, language = {en} } @inproceedings{KahntRammLameckeretal.2012, author = {Kahnt, Max and Ramm, Heiko and Lamecker, Hans and Zachow, Stefan}, title = {Feature-Preserving, Multi-Material Mesh Generation using Hierarchical Oracles}, volume = {7599}, booktitle = {Proc. MICCAI Workshop on Mesh Processing in Medical Image Analysis (MeshMed)}, editor = {Levine, Joshua A. and Paulsen, Rasmus R. and Zhang, Yongjie}, pages = {101 -- 111}, year = {2012}, language = {en} } @misc{LangeLameckerHuenerbeinetal.2008, author = {Lange, Thomas and Lamecker, Hans and H{\"u}nerbein, Michael and Eulenstein, Sebastian and Beller, Sigfried and Schlag, Peter}, title = {Validation Metrics for Non-Rigid Registration of Medical Images containing Vessel Trees}, publisher = {Springer}, doi = {10.1007/978-3-540-78640-5_17}, pages = {82 -- 86}, year = {2008}, language = {en} } @inproceedings{KainmuellerLameckerSeimetal.2009, author = {Kainm{\"u}ller, Dagmar and Lamecker, Hans and Seim, Heiko and Zinser, Max and Zachow, Stefan}, title = {Automatic Extraction of Mandibular Nerve and Bone from Cone-Beam CT Data}, booktitle = {Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI)}, editor = {Yang, Guang-Zhong and J. Hawkes, David and Rueckert, Daniel and Noble, J. Alison and J. Taylor, Chris}, address = {London, UK}, pages = {76 -- 83}, year = {2009}, language = {en} }