TY - THES A1 - Lamecker, Hans T1 - Variational and statistical shape modeling for 3D geometry reconstruction N2 - The reconstruction of geometric shapes plays an important role in many biomedical applications. One example is the patient-specific, computer-aided planning of complex interventions, which requires the generation of explicitly represented geometric models of anatomical structures from medical image data. Only solutions that require minimal interaction by medical personnel are likely to enter clinical routine. Another example is the planning of surgical corrections of deformities where the target shape is unknown. Surgeons are often forced to resort to subjective criteria. These applications still pose highly challenging reconstruction problems, which are addressed in this thesis. The fundamental hypothesis, pursued in this thesis, is that the problems can be solved by incorporating a-priori knowledge about shape and other application-specific characteristics. Here, we focus mainly on the aspect of geometric shape analysis. The basic idea is to capture the most essential variations of a certain class of geometric objects via statistical shape models, which model typical features contained in a given population, and restrict the outcome of a reconstruction algorithm (more or less) to the space spanned by such models. A fundamental prerequisite for performing statistical shape analysis on a set of different objects is the identification of corresponding points on their associated surfaces. This problem is particularly difficult to solve if the shapes stem from different individuals. The reason lies in the basic difficulty of defining suitable measures of similarity. In this thesis, we divide the correspondence problem into feature and non-feature matching. The feature part depends on the application, while the non-feature part can be characterized by a purely geometric description. We propose two different approaches. The first approach has proved useful in many applications. Yet, it suffers from some practical limitations and does not yield a measure of similarity. Our second, variational, approach is designed to overcome these limitations. In it, we propose to minimize an invariant stretching measure, constrained by previously computed features. An important property, which sets our method apart from previous work, is that it does not require the computation of a global surface parameterization. KW - correspondence problem KW - surface matching KW - image segmentation KW - model-based shape reconstruction KW - 2d-3d shape reconstruction Y1 - 2008 UR - http://www.diss.fu-berlin.de/diss/receive/FUDISS_thesis_000000005805 ER - TY - GEN A1 - Günther, Andreas A1 - Lamecker, Hans A1 - Weiser, Martin T1 - Direct LDDMM of Discrete Currents with Adaptive Finite Elements N2 - We consider Large Deformation Diffeomorphic Metric Mapping of general $m$-currents. After stating an optimization algorithm in the function space of admissable morph generating velocity fields, two innovative aspects in this framework are presented and numerically investigated: First, we spatially discretize the velocity field with conforming adaptive finite elements and discuss advantages of this new approach. Second, we directly compute the temporal evolution of discrete $m$-current attributes. T3 - ZIB-Report - 11-22 KW - Large Deformation KW - Diffeomorphic Registration KW - Matching KW - Currents KW - Adaptive Finite Elements Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-13090 ER - TY - GEN A1 - Zilske, Michael A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Adaptive Remeshing of Non-Manifold Surfaces N2 - 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. T3 - ZIB-Report - 07-01 KW - remeshing KW - non-manifold KW - mesh quality optimization Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-9445 ER - TY - GEN A1 - Ehlke, Moritz A1 - Ramm, Heiko A1 - Lamecker, Hans A1 - Hege, Hans-Christian A1 - Zachow, Stefan T1 - Fast Generation of Virtual X-ray Images from Deformable Tetrahedral Meshes N2 - We propose a novel GPU-based approach to render virtual X-ray projections of deformable tetrahedral meshes. These meshes represent the shape and the internal density distribution of a particular anatomical structure and are derived from statistical shape and intensity models (SSIMs). We apply our method to improve the geometric reconstruction of 3D anatomy (e.g.\ pelvic bone) from 2D X-ray images. For that purpose, shape and density of a tetrahedral mesh are varied and virtual X-ray projections are generated within an optimization process until the similarity between the computed virtual X-ray and the respective anatomy depicted in a given clinical X-ray is maximized. The OpenGL implementation presented in this work deforms and projects tetrahedral meshes of high resolution (200.000+ tetrahedra) at interactive rates. It generates virtual X-rays that accurately depict the density distribution of an anatomy of interest. Compared to existing methods that accumulate X-ray attenuation in deformable meshes, our novel approach significantly boosts the deformation/projection performance. The proposed projection algorithm scales better with respect to mesh resolution and complexity of the density distribution, and the combined deformation and projection on the GPU scales better with respect to the number of deformation parameters. The gain in performance allows for a larger number of cycles in the optimization process. Consequently, it reduces the risk of being stuck in a local optimum. We believe that our approach contributes in orthopedic surgery, where 3D anatomy information needs to be extracted from 2D X-rays to support surgeons in better planning joint replacements. T3 - ZIB-Report - 13-38 KW - digitally reconstructed radiographs KW - volume rendering KW - mesh deformation KW - statistical shape and intensity models KW - image registration KW - GPU acceleration Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-41896 SN - 1438-0064 ER - TY - CHAP A1 - Lamecker, Hans A1 - Lange, Thomas A1 - Seebaß, Martin T1 - Erzeugung statistischer 3D-Formmodelle zur Segmentierung medizinischer Bilddaten T2 - Proc. BVM 2003 Y1 - 2003 SP - 398 EP - 403 PB - Springer CY - Erlangen ER - TY - CHAP A1 - Lange, Thomas A1 - Lamecker, Hans A1 - Seebaß, Martin T1 - Ein Softwarepaket für die modellbasierte Segmentierung anatomischer Strukturen T2 - Proc. BVM 2003 Y1 - 2003 SP - 111 EP - 116 CY - Erlangen ER - TY - CHAP A1 - Lamecker, Hans A1 - Lange, Thomas A1 - Seebaß, Martin A1 - Eulenstein, Sebastian A1 - Westerhoff, Malte A1 - Hege, Hans-Christian T1 - Automatic Segmentation of the Liver for Preoperative Planning of Resections T2 - Proc. MMVR 2003 Y1 - 2003 SP - 171 EP - 173 PB - IOS press CY - Newport Beach, CA ER - TY - CHAP A1 - Lange, Thomas A1 - Lamecker, Hans A1 - Eulenstein, Sebastian T1 - Automatische Segmentierung von Lebergewebe für die präoperative Planung von Resektionen T2 - 1. Jahrestagung der Deutschen Gesellschaft für Computer- Roboterassistierte Chirugie CURAC Y1 - 2002 ER - TY - CHAP A1 - Seebaß, Martin A1 - Lamecker, Hans A1 - Gellermann, Johanna A1 - Wust, Peter T1 - The impact of segmentation accuracy on treatment planning for regional hyperthermia T2 - Proceedings of the 9th International Congress on Hyperthermic Oncology Y1 - 2004 ER - TY - CHAP A1 - Eulenstein, Sebastian A1 - Lange, Thomas A1 - Hünerbein, Michael A1 - Schlag, Peter A1 - Lamecker, Hans T1 - Ultrasound based navigation system incorporating preoperative planning for liver surgery T2 - Proceedings CARS Y1 - 2004 U6 - https://doi.org/10.1016/j.ics.2004.03.121 target VL - 1268 SP - 758 EP - 763 PB - Elsevier ER - TY - CHAP A1 - Lange, Thomas A1 - Tunn, Per-Ulf A1 - Lamecker, Hans A1 - Scheinemann, Peter A1 - Eulenstein, Sebastian A1 - Schlag, Peter-Michael ED - Tolxdorff, Thomas T1 - Computerunterstützte Prothesenkonstruktion mittels statistischem Formmodell bei Beckenresektion T2 - Proceedings BVM, Informatik aktuell Y1 - 2004 SP - 30 EP - 34 PB - Springer ER - TY - CHAP A1 - Lamecker, Hans A1 - Seebaß, Martin A1 - Hege, Hans-Christian A1 - Deuflhard, Peter ED - Fitzpatrick, J. ED - Sonka, Milan T1 - A 3D statistical shape model of the pelvic bone for segmentation T2 - Proceedings of SPIE - Volume 5370 Medical Imaging 2004 Y1 - 2004 U6 - https://doi.org/10.1117/12.534145 SP - 1341 EP - 1351 ER - TY - CHAP A1 - Seebaß, Martin A1 - Lamecker, Hans A1 - Lange, Thomas A1 - Gellermann, Johanna A1 - Wust, Peter T1 - A Statistical Shape Model of the Pelvic Bone for Segmentation T2 - European Society for Hyperthermic Oncology Annual Meeting Proceedings Y1 - 2003 SP - 91 EP - 92 ER - TY - CHAP A1 - Hege, Hans-Christian A1 - Schirmacher, Hartmut A1 - Westerhoff, Malte A1 - Lamecker, Hans A1 - Prohaska, Steffen A1 - Zachow, Stefan T1 - From Image Data to Three-Dimensional Models - Case Studies on the Impact of 3D Patient Models T2 - Proceedings of the Japan Korea Computer Graphics Conference 2002 Y1 - 2002 PB - Kanazawa University CY - Kanazawa City, Ishikawa, Japan ER - TY - CHAP A1 - Lamecker, Hans A1 - Lange, Thomas A1 - Seebaß, Martin T1 - A Statistical Shape Model for the Liver T2 - MICCAI 2002 Y1 - 2002 U6 - https://doi.org/10.1007/3-540-45787-9_53 SP - 422 EP - 427 PB - Springer 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 - JOUR A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hege, Hans-Christian A1 - Zöckler, Maja T1 - Surgical treatment of craniosynostosis based on a statistical 3D-shape model JF - Int. J. Computer Assisted Radiology and Surgery Y1 - 2006 U6 - https://doi.org/10.1007/s11548-006-0024-x VL - 1(1) SP - 253 EP - 254 ER - TY - JOUR A1 - Zachow, Stefan A1 - Lamecker, Hans A1 - Elsholtz, Barbara A1 - Stiller, Michael T1 - Is the course of the mandibular nerve deducible from the shape of the mandible? JF - Int. J. of Computer Assisted Radiology and Surgery Y1 - 2006 SP - 415 EP - 417 PB - Springer ER - TY - GEN A1 - Ehlke, Moritz A1 - Ramm, Heiko A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Efficient projection and deformation of volumetric intensity models for accurate simulation of X-ray images N2 - We present an efficient GPU-based method to generate virtual X-ray images from tetrahedral meshes which are associated with attenuation values. In addition, a novel approach is proposed that performs the model deformation on the GPU. The tetrahedral grids are derived from volumetric statistical shape and intensity models (SSIMs) and describe anatomical structures. Our research targets at reconstructing 3D anatomical shapes by comparing virtual X-ray images generated using our novel approach with clinical data while varying the shape and density of the SSIM in an optimization process. We assume that a deformed SSIM adequately represents an anatomy of interest when the similarity between the virtual and the clinical X-ray image is maximized. The OpenGL implementation presented here generates accurate (virtual) X-ray images at interactive rates, thus qualifying it for its use in the reconstruction process. T3 - ZIB-Report - 12-40 KW - Digitally Reconstructed Radiograph (DRR), Anatomy Reconstruction, Statistical Shape and Intensity Model (SSIM), GPU acceleration Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-16580 SN - 1438-0064 ER - TY - GEN A1 - Ramm, Heiko A1 - Morillo Victoria, Oscar Salvador A1 - Todt, Ingo A1 - Schirmacher, Hartmut A1 - Ernst, Arneborg A1 - Zachow, Stefan A1 - Lamecker, Hans T1 - Visual Support for Positioning Hearing Implants N2 - We present a software planning tool that provides intuitive visual feedback for finding suitable positions of hearing implants in the human temporal bone. After an automatic reconstruction of the temporal bone anatomy the tool pre-positions the implant and allows the user to adjust its position interactively with simple 2D dragging and rotation operations on the bone's surface. During this procedure, visual elements like warning labels on the implant or color encoded bone density information on the bone geometry provide guidance for the determination of a suitable fit. T3 - ZIB-Report - 13-53 KW - bone anchored hearing implant KW - surgery planning KW - segmentation KW - visualization Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-42495 SN - 1438-0064 ER - TY - GEN A1 - Ambellan, Felix A1 - Lamecker, Hans A1 - von Tycowicz, Christoph A1 - Zachow, Stefan T1 - Statistical Shape Models - Understanding and Mastering Variation in Anatomy N2 - In our chapter we are describing how to reconstruct three-dimensional anatomy from medical image data and how to build Statistical 3D Shape Models out of many such reconstructions yielding a new kind of anatomy that not only allows quantitative analysis of anatomical variation but also a visual exploration and educational visualization. Future digital anatomy atlases will not only show a static (average) anatomy but also its normal or pathological variation in three or even four dimensions, hence, illustrating growth and/or disease progression. Statistical Shape Models (SSMs) are geometric models that describe a collection of semantically similar objects in a very compact way. SSMs represent an average shape of many three-dimensional objects as well as their variation in shape. The creation of SSMs requires a correspondence mapping, which can be achieved e.g. by parameterization with a respective sampling. If a corresponding parameterization over all shapes can be established, variation between individual shape characteristics can be mathematically investigated. We will explain what Statistical Shape Models are and how they are constructed. Extensions of Statistical Shape Models will be motivated for articulated coupled structures. In addition to shape also the appearance of objects will be integrated into the concept. Appearance is a visual feature independent of shape that depends on observers or imaging techniques. Typical appearances are for instance the color and intensity of a visual surface of an object under particular lighting conditions, or measurements of material properties with computed tomography (CT) or magnetic resonance imaging (MRI). A combination of (articulated) statistical shape models with statistical models of appearance lead to articulated Statistical Shape and Appearance Models (a-SSAMs).After giving various examples of SSMs for human organs, skeletal structures, faces, and bodies, we will shortly describe clinical applications where such models have been successfully employed. Statistical Shape Models are the foundation for the analysis of anatomical cohort data, where characteristic shapes are correlated to demographic or epidemiologic data. SSMs consisting of several thousands of objects offer, in combination with statistical methods ormachine learning techniques, the possibility to identify characteristic clusters, thus being the foundation for advanced diagnostic disease scoring. T3 - ZIB-Report - 19-13 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-72699 SN - 1438-0064 ER - TY - CHAP A1 - Ambellan, Felix A1 - Lamecker, Hans A1 - von Tycowicz, Christoph A1 - Zachow, Stefan ED - Rea, Paul M. T1 - Statistical Shape Models - Understanding and Mastering Variation in Anatomy T2 - Biomedical Visualisation N2 - In our chapter we are describing how to reconstruct three-dimensional anatomy from medical image data and how to build Statistical 3D Shape Models out of many such reconstructions yielding a new kind of anatomy that not only allows quantitative analysis of anatomical variation but also a visual exploration and educational visualization. Future digital anatomy atlases will not only show a static (average) anatomy but also its normal or pathological variation in three or even four dimensions, hence, illustrating growth and/or disease progression. Statistical Shape Models (SSMs) are geometric models that describe a collection of semantically similar objects in a very compact way. SSMs represent an average shape of many three-dimensional objects as well as their variation in shape. The creation of SSMs requires a correspondence mapping, which can be achieved e.g. by parameterization with a respective sampling. If a corresponding parameterization over all shapes can be established, variation between individual shape characteristics can be mathematically investigated. We will explain what Statistical Shape Models are and how they are constructed. Extensions of Statistical Shape Models will be motivated for articulated coupled structures. In addition to shape also the appearance of objects will be integrated into the concept. Appearance is a visual feature independent of shape that depends on observers or imaging techniques. Typical appearances are for instance the color and intensity of a visual surface of an object under particular lighting conditions, or measurements of material properties with computed tomography (CT) or magnetic resonance imaging (MRI). A combination of (articulated) statistical shape models with statistical models of appearance lead to articulated Statistical Shape and Appearance Models (a-SSAMs).After giving various examples of SSMs for human organs, skeletal structures, faces, and bodies, we will shortly describe clinical applications where such models have been successfully employed. Statistical Shape Models are the foundation for the analysis of anatomical cohort data, where characteristic shapes are correlated to demographic or epidemiologic data. SSMs consisting of several thousands of objects offer, in combination with statistical methods ormachine learning techniques, the possibility to identify characteristic clusters, thus being the foundation for advanced diagnostic disease scoring. Y1 - 2019 SN - 978-3-030-19384-3 SN - 978-3-030-19385-0 U6 - https://doi.org/10.1007/978-3-030-19385-0_5 VL - 3 IS - 1156 SP - 67 EP - 84 PB - Springer Nature Switzerland AG ET - 1 ER - 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 - Lamecker, Hans A1 - Seebaß, Martin A1 - Hege, Hans-Christian A1 - Deuflhard, Peter ED - Fitzpatrick, J. ED - Sonka, Milan T1 - A 3d statistical shape model of the pelvic bone for segmentation T2 - Proc. SPIE Medical Imaging 2004 Y1 - 2004 VL - 5370 SP - 1341 EP - 1351 ER - TY - CHAP A1 - Lamecker, Hans A1 - Seebass, Martin A1 - Lange, Thomas A1 - Hege, Hans-Christian A1 - Deuflhard, Peter ED - Westwood, J. T1 - Visualization of the variability of 3D statistical shape models by animation T2 - Proc. Medicine Meets Virtual Reality Y1 - 2004 VL - 98 SP - 190 EP - 196 PB - IOS Press 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 - JOUR A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Heller, Markus O. A1 - Weber, Britta A1 - Hege, Hans-Christian A1 - Zachow, Stefan T1 - Omnidirectional Displacements for Deformable Surfaces JF - Medical Image Analysis Y1 - 2013 U6 - https://doi.org/10.1016/j.media.2012.11.006 VL - 17 IS - 4 SP - 429 EP - 441 PB - Elsevier 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 - JOUR A1 - Ehlke, Moritz A1 - Ramm, Heiko A1 - Lamecker, Hans A1 - Hege, Hans-Christian A1 - Zachow, Stefan T1 - Fast Generation of Virtual X-ray Images for Reconstruction of 3D Anatomy JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-35928 VL - 19 IS - 12 SP - 2673 EP - 2682 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 - JOUR A1 - Dworzak, Jalda A1 - Lamecker, Hans A1 - von Berg, Jens A1 - Klinder, Tobias A1 - Lorenz, Cristian A1 - Kainmüller, Dagmar A1 - Seim, Heiko A1 - Hege, Hans-Christian A1 - Zachow, Stefan T1 - 3D Reconstruction of the Human Rib Cage from 2D Projection Images using a Statistical Shape Model JF - Int. J. Comput. Assist. Radiol. Surg. Y1 - 2010 U6 - https://doi.org/10.1007/s11548-009-0390-2 SN - 1861-6410 VL - 5 IS - 2 SP - 111 EP - 124 PB - Springer ER - TY - JOUR A1 - Rybak, Jürgen A1 - Kuß, Anja A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hege, Hans-Christian A1 - Lienhard, Matthias A1 - Singer, Jochen A1 - Neubert, Kerstin A1 - Menzel, Randolf T1 - The Digital Bee Brain: Integrating and Managing Neurons in a Common 3D Reference System JF - Front. Syst. Neurosci. Y1 - 2010 U6 - https://doi.org/10.3389/fnsys.2010.00030 VL - 4 IS - 30 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 - Bindernagel, Matthias A1 - Kainmüller, Dagmar A1 - Seim, Heiko A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hege, Hans-Christian T1 - An Articulated Statistical Shape Model of the Human Knee T2 - Bildverarbeitung für die Medizin 2011 Y1 - 2011 U6 - https://doi.org/10.1007/978-3-642-19335-4_14 SP - 59 EP - 63 PB - Springer 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 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Seim, Heiko A1 - Zachow, Stefan A1 - Hege, Hans-Christian ED - Navab, Tianzi ED - P. W. Pluim, Josien ED - Viergever, Max T1 - Improving Deformable Surface Meshes through Omni-directional Displacements and MRFs T2 - Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI) Y1 - 2010 U6 - https://doi.org/10.1007/978-3-642-15705-9_28 VL - 6361 SP - 227 EP - 234 PB - Springer ER -