TY - JOUR A1 - Hildebrandt, Thomas A1 - Bruening, Jan Joris A1 - Schmidt, Nora Laura A1 - Lamecker, Hans A1 - Heppt, Werner A1 - Zachow, Stefan A1 - Goubergrits, Leonid T1 - The Healthy Nasal Cavity - Characteristics of Morphology and Related Airflow Based on a Statistical Shape Model Viewed from a Surgeon’s Perspective JF - Facial Plastic Surgery N2 - Functional surgery on the nasal framework requires referential criteria to objectively assess nasal breathing for indication and follow-up. Thismotivated us to generate amean geometry of the nasal cavity based on a statistical shape model. In this study, the authors could demonstrate that the introduced nasal cavity’s mean geometry features characteristics of the inner shape and airflow, which are commonly observed in symptom-free subjects. Therefore, the mean geometry might serve as a reference-like model when one considers qualitative aspects. However, to facilitate quantitative considerations and statistical inference, further research is necessary. Additionally, the authorswere able to obtain details about the importance of the isthmus nasi and the inferior turbinate for the intranasal airstream. KW - statistical shape model KW - nasal cavity KW - nasal breathing KW - nasal airflow KW - isthmus nasi KW - inferior turbinate Y1 - 2019 U6 - https://doi.org/10.1055/s-0039-1677721 VL - 35 IS - 1 SP - 9 EP - 13 ER - TY - JOUR A1 - Hildebrandt, Thomas A1 - Bruening, Jan Joris A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Heppt, Werner A1 - Schmidt, Nora A1 - Goubergrits, Leonid T1 - Digital Analysis of Nasal Airflow Facilitating Decision Support in Rhinosurgery JF - Facial Plastic Surgery N2 - Successful functional surgery on the nasal framework requires reliable and comprehensive diagnosis. In this regard, the authors introduce a new methodology: Digital Analysis of Nasal Airflow (diANA). It is based on computational fluid dynamics, a statistical shape model of the healthy nasal cavity and rhinologic expertise. diANA necessitates an anonymized tomographic dataset of the paranasal sinuses including the complete nasal cavity and, when available, clinical information. The principle of diANA is to compare the morphology and the respective airflow of an individual nose with those of a reference. This enablesmorphometric aberrations and consecutive flow field anomalies to localize and quantify within a patient’s nasal cavity. Finally, an elaborated expert opinion with instructive visualizations is provided. Using diANA might support surgeons in decision-making, avoiding unnecessary surgery, gaining more precision, and target-orientation for indicated operations. KW - nasal airflow simulation KW - nasal breathing KW - statistical shape model KW - diANA KW - nasal obstruction KW - rhinorespiratory homeostasis Y1 - 2019 U6 - https://doi.org/10.1055/s-0039-1677720 VL - 35 IS - 1 SP - 1 EP - 8 ER - TY - JOUR A1 - Hettich, G. A1 - Schierjott, R. A. A1 - Schilling, C. A1 - Maas, A. A1 - Ramm, Heiko A1 - Bindernagel, Matthias A1 - Lamecker, Hans A1 - Grupp, T. M. T1 - Validation of a Statistical Shape Model for Acetabular Bone Defect Analysis JF - ISTA 2018 London Abstract Book N2 - Acetabular bone defects are still challenging to quantify. Numerous classification schemes have been proposed to categorize the diverse kinds of defects. However, these classification schemes are mainly descriptive and hence it remains difficult to apply them in pre-clinical testing, implant development and pre-operative planning. By reconstructing the native situation of a defect pelvis using a Statistical Shape Model (SSM), a more quantitative analysis of the bone defects could be performed. The aim of this study is to develop such a SSM and to validate its accuracy using relevant clinical scenarios and parameters. Y1 - 2018 ER - TY - JOUR A1 - Bernard, Florian A1 - Salamanca, Luis A1 - Thunberg, Johan A1 - Tack, Alexander A1 - Jentsch, Dennis A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hertel, Frank A1 - Goncalves, Jorge A1 - Gemmar, Peter T1 - Shape-aware Surface Reconstruction from Sparse Data JF - arXiv N2 - 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. Y1 - 2016 SP - 1602.08425v1 ER - TY - JOUR A1 - Bernard, Florian A1 - Salamanca, Luis A1 - Thunberg, Johan A1 - Tack, Alexander A1 - Jentsch, Dennis A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hertel, Frank A1 - Goncalves, Jorge A1 - Gemmar, Peter T1 - Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds JF - Medical Image Analysis N2 - 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. Y1 - 2017 UR - http://www.sciencedirect.com/science/article/pii/S1361841517300233 U6 - https://doi.org/10.1016/j.media.2017.02.005 VL - 38 SP - 77 EP - 89 ER - TY - CHAP A1 - Ramm, Heiko A1 - Victoria Morillo, Oscar Salvador A1 - Todt, Ingo A1 - Schirmacher, Hartmut A1 - Ernst, Arneborg A1 - Zachow, Stefan A1 - Lamecker, Hans ED - Freysinger, Wolfgang T1 - Visual Support for Positioning Hearing Implants T2 - Proceedings of the 12th annual meeting of the CURAC society Y1 - 2013 SP - 116 EP - 120 ER - TY - CHAP A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Wilson, Dave A1 - Anglin, Carolyn A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data T2 - Proceedings of the Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC) N2 - 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. KW - Total Knee Arthoplasty KW - Sparse Geometry Reconstruction KW - Statistical Shape Models Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-65339 VL - 16 SP - 24 EP - 30 ER - TY - GEN A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Wilson, Dave A1 - Anglin, Carolyn A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data N2 - 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. T3 - ZIB-Report - 17-71 KW - Knee Arthroplasty KW - Sparse Geometry Reconstruction KW - Statistical Shape Models Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-66052 SN - 1438-0064 ER - TY - JOUR A1 - Brüning, Jan A1 - Hildebrandt, Thomas A1 - Heppt, Werner A1 - Schmidt, Nora A1 - Lamecker, Hans A1 - Szengel, Angelika A1 - Amiridze, Natalja A1 - Ramm, Heiko A1 - Bindernagel, Matthias A1 - Zachow, Stefan A1 - Goubergrits, Leonid T1 - Characterization of the Airflow within an Average Geometry of the Healthy Human Nasal Cavity JF - Scientific Reports N2 - 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. Y1 - 2020 UR - https://rdcu.be/b2irD U6 - https://doi.org/10.1038/s41598-020-60755-3 VL - 3755 IS - 10 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 - CHAP A1 - Estacio, Laura A1 - Ehlke, Moritz A1 - Tack, Alexander A1 - Castro-Gutierrez, Eveling A1 - Lamecker, Hans A1 - Mora, Rensso A1 - Zachow, Stefan T1 - Unsupervised Detection of Disturbances in 2D Radiographs T2 - 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) N2 - 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. Y1 - 2021 U6 - https://doi.org/10.1109/ISBI48211.2021.9434091 SP - 367 EP - 370 ER - TY - JOUR A1 - Sekuboyina, Anjany A1 - Bayat, Amirhossein A1 - Husseini, Malek E. A1 - Löffler, Maximilian A1 - Li, Hongwei A1 - Tetteh, Giles A1 - Kukačka, Jan A1 - Payer, Christian A1 - Štern, Darko A1 - Urschler, Martin A1 - Chen, Maodong A1 - Cheng, Dalong A1 - Lessmann, Nikolas A1 - Hu, Yujin A1 - Wang, Tianfu A1 - Yang, Dong A1 - Xu, Daguang A1 - Ambellan, Felix A1 - Amiranashvili, Tamaz A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Lehnert, Sebastian A1 - Lirio, Marilia A1 - de Olaguer, Nicolás Pérez A1 - Ramm, Heiko A1 - Sahu, Manish A1 - Tack, Alexander A1 - Zachow, Stefan A1 - Jiang, Tao A1 - Ma, Xinjun A1 - Angerman, Christoph A1 - Wang, Xin A1 - Wei, Qingyue A1 - Brown, Kevin A1 - Wolf, Matthias A1 - Kirszenberg, Alexandre A1 - Puybareau, Élodie A1 - Valentinitsch, Alexander A1 - Rempfler, Markus A1 - Menze, Björn H. A1 - Kirschke, Jan S. T1 - VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images JF - arXiv Y1 - 2020 ER - TY - GEN A1 - Lamecker, Hans A1 - Lange, Thomas A1 - Seebass, Martin T1 - Segmentation of the Liver using a 3D Statistical Shape Model N2 - 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. T3 - ZIB-Report - 04-09 KW - Statistical Shape Model KW - 3D Correspondence Problem KW - Model-Based Segmentation KW - Diffusion Filtering KW - Geometric Distortion KW - Surface Distance Y1 - 2004 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-7847 ER - TY - GEN A1 - Grewe, Carl Martin A1 - Lamecker, Hans A1 - Zachow, Stefan ED - Hermanussen, Michael T1 - Landmark-based Statistical Shape Analysis T2 - Auxology - Studying Human Growth and Development url Y1 - 2013 UR - http://www.schweizerbart.de/publications/detail/isbn/9783510652785 SP - 199 EP - 201 PB - Schweizerbart Verlag, Stuttgart ER - TY - GEN A1 - Grewe, Carl Martin A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Digital morphometry: The Potential of Statistical Shape Models T2 - Anthropologischer Anzeiger. Journal of Biological and Clinical Anthropology Y1 - 2011 SP - 506 EP - 506 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 - von Berg, Jens A1 - Dworzak, Jalda A1 - Klinder, Tobias A1 - Manke, Dirk A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Lorenz, Cristian T1 - Temporal Subtraction of Chest Radiographs Compensating Pose Differences T2 - SPIE Medical Imaging Y1 - 2011 ER - TY - CHAP A1 - Kahnt, Max A1 - Galloway, Francis A1 - Seim, Heiko A1 - Lamecker, Hans A1 - Taylor, Mark A1 - Zachow, Stefan T1 - Robust and Intuitive Meshing of Bone-Implant Compounds T2 - CURAC Y1 - 2011 SP - 71 EP - 74 CY - Magdeburg ER - TY - JOUR A1 - Lamecker, Hans A1 - Pennec, Xavier T1 - Atlas to Image-with-Tumor Registration based on Demons and Deformation Inpainting JF - Proc. MICCAI Workshop on Computational Imaging Biomarkers for Tumors - From Qualitative to Quantitative (CIBT’2010) Y1 - 2010 CY - Beijing, China ER - TY - CHAP A1 - Seim, Heiko A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Bindernagel, Matthias A1 - Malinowski, Jana A1 - Zachow, Stefan ED - v. Ginneken, B. T1 - Model-based Auto-Segmentation of Knee Bones and Cartilage in MRI Data T2 - Proc. MICCAI Workshop Medical Image Analysis for the Clinic Y1 - 2010 SP - 215 EP - 223 ER - TY - CHAP A1 - Zachow, Stefan A1 - Kubiack, Kim A1 - Malinowski, Jana A1 - Lamecker, Hans A1 - Essig, Harald A1 - Gellrich, Nils-Claudius T1 - Modellgestützte chirurgische Rekonstruktion komplexer Mittelgesichtsfrakturen T2 - Proc. BMT, Biomed Tech 2010 Y1 - 2010 VL - 55 (Suppl 1) SP - 107 EP - 108 PB - Walter de Gruyter-Verlag ER - TY - GEN A1 - Bindernagel, Matthias A1 - Kainmüller, Dagmar A1 - Ramm, Heiko A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Analysis of inter-individual anatomical shape variations of joint structures T2 - Proc. Int. Society of Computer Assisted Orthopaedic Surgery (CAOS) Y1 - 2012 IS - 210 ER -