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 - 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 - 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 - 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 -