TY - CHAP A1 - Lamecker, Hans A1 - Zöckler, Maja A1 - Haberl, Hannes A1 - Zachow, Stefan A1 - Hege, Hans-Christian T1 - Statistical shape modeling for craniosynostosis planning T2 - 2nd International Conference Advanced Digital Technology in Head and Neck Reconstruction 2005, Abstract Volume Y1 - 2005 SP - 64 CY - Banff, Alberta ER - TY - CHAP A1 - Lamecker, Hans A1 - Wenckebach, Thomas A1 - Hege, Hans-Christian T1 - Atlas-based 3D-shape reconstruction from x-ray images T2 - Proc. Int. Conf. of Pattern Recognition (ICPR2006) Y1 - 2006 U6 - https://doi.org/10.1109/ICPR.2006.279 VL - Volume I SP - 371 EP - 374 PB - IEEE Computer Society ER - TY - CHAP A1 - Lamecker, Hans A1 - Wenckebach, Thomas A1 - Hege, Hans-Christian A1 - N. Duda, Georg A1 - Heller, Markus T1 - Atlas-basierte 3D-Rekonstruktion des Beckens aus 2D-Projektionsbildern T2 - Bildverarbeitung für die Medizin 2006: Algorithmen - Systeme - Anwendungen Y1 - 2006 U6 - https://doi.org/10.1007/3-540-32137-3_6 SP - 26 EP - 30 PB - Springer Berlin Heidelberg ER - TY - CHAP A1 - Baum, Daniel A1 - Mahlow, Kristin A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Müller, Johannes A1 - Hege, Hans-Christian T1 - The Potential of Surface-based Geometric Morphometrics for Evolutionary Studies: An Example using Dwarf Snakes (Eirenis) T2 - Abstract in DigitalSpecimen 2014 N2 - Geometric morphometrics plays an important role in evolutionary studies. The state-of-the-art in this field are landmark-based methods. Since the landmarks usually need to be placed manually, only a limited number of landmarks are generally used to represent the shape of an anatomical structure. As a result, shape characteristics that cannot be properly represented by small sets of landmarks are disregarded. In this study, we present a method that is free of this limitation. The method takes into account the whole shape of an anatomical structure, which is represented as a surface, hence the term ‘surface-based morphometrics’. Correspondence between two surfaces is established by defining a partitioning of the surfaces into homologous surface patches. The first step for the generation of a surface partitioning is to place landmarks on the surface. Subsequently, the landmarks are connected by curves lying on the surface. The curves, called ‘surface paths’, might either follow specific anatomical features or they can be geodesics, that is, shortest paths on the surface. One important requirement, however, is that the resulting surface path networks are topologically equivalent across all surfaces. Once the surface path networks have been defined, the surfaces are decomposed into patches according to the path networks. This approach has several advantages. One of them is that we can discretize the surface by as many points as desired. Thus, even fine shape details can be resolved if this is of interest for the study. Since a point discretization is used, another advantage is that well-established analysis methods for landmark-based morphometrics can be utilized. Finally, the shapes can be easily morphed into one another, thereby greatly supporting the understanding of shape changes across all considered specimens. To show the potential of the described method for evolutionary studies of biological specimens, we applied the method to the para-basisphenoid complex of the snake genus Eirenis. By using this anatomical structure as example, we present all the steps that are necessary for surface-based morphometrics, including the segmentation of the para-basisphenoid complex from micro-CT data sets. We also show some first results using statistical analysis as well as classification methods based on the presented technique. Y1 - 2014 ER - TY - GEN A1 - Lamecker, Hans A1 - Hege, Hans-Christian A1 - Tabelow, Karsten A1 - Polzehl, Jörg ED - Deuflhard, Peter ED - et al., T1 - Image Processing T2 - MATHEON - Mathematics for Key Technologies Y1 - 2014 U6 - https://doi.org/10.4171/137 SP - 359 EP - 376 PB - European Mathematical Society ER - TY - JOUR A1 - Sekuboyina, Anjany A1 - Husseini, Malek E. A1 - Bayat, Amirhossein A1 - Löffler, Maximilian A1 - Liebl, Hans 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 - Brown, Kevin A1 - Kirszenberg, Alexandre A1 - Puybareau, Élodie A1 - Chen, Di A1 - Bai, Yiwei A1 - Rapazzo, Brandon H. A1 - Yeah, Timyoas A1 - Zhang, Amber A1 - Xu, Shangliang A1 - Hou, Feng A1 - He, Zhiqiang A1 - Zeng, Chan A1 - Xiangshang, Zheng A1 - Liming, Xu A1 - Netherton, Tucker J. A1 - Mumme, Raymond P. A1 - Court, Laurence E. A1 - Huang, Zixun A1 - He, Chenhang A1 - Wang, Li-Wen A1 - Ling, Sai Ho A1 - Huynh, Lê Duy A1 - Boutry, Nicolas A1 - Jakubicek, Roman A1 - Chmelik, Jiri A1 - Mulay, Supriti A1 - Sivaprakasam, Mohanasankar A1 - Paetzold, Johannes C. A1 - Shit, Suprosanna A1 - Ezhov, Ivan A1 - Wiestler, Benedikt A1 - Glocker, Ben 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 - Medical Image Analysis N2 - Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse. Y1 - 2021 U6 - https://doi.org/10.1016/j.media.2021.102166 VL - 73 ER - 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 - 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 - 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 - 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 - 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 - 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 -