@misc{DeuflhardDoesselLouisetal.2008, author = {Deuflhard, Peter and D{\"o}ssel, Olaf and Louis, Alfred and Zachow, Stefan}, title = {Mehr Mathematik wagen in der Medizin}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-10776}, number = {08-25}, year = {2008}, abstract = {Der Artikel gibt einen Einblick in das reiche Feld der Zusammenarbeit zwischen Mathematik und Medizin. Beispielhaft werden drei Erfolgsmodelle dargestellt: Medizinische Bildgebung, mathematische Modellierung und Biosignalverarbeitung im Bereich der Dynamik des Herzens sowie mathematische Modellierung und Simulation in der Krebstherapie Hyperthermie und der Mund-Kiefer-Gesichts-Chirurgie. In allen F{\"a}llen existiert ein Gleichklang der Interessen von Medizin und Mathematik: Beide Disziplinen wollen die Resultate schnell und zuverl{\"a}ssig. F{\"u}r die Klinik heißt das, dass notwendige Rechnungen in m{\"o}glichst kurzer Zeit, und zwar auf dem PC, ablaufen m{\"u}ssen und dass die Resultate so genau und belastbar sein m{\"u}ssen, dass medizinische Entscheidungen darauf aufbauen k{\"o}nnen. F{\"u}r die Mathematik folgt daraus, dass h{\"o}chste Anforderungen an die Effizienz der verwendeten Algorithmen und die darauf aufbauende Software in Numerik und Visualisierung zu stellen sind. Jedes Kapitel endet mit einer Darstellung der Perspektive des jeweiligen Gebietes. Abschließend werden m{\"o}gliche Handlungsoptionen f{\"u}r Politik und Wirtschaft diskutiert.}, language = {de} } @misc{ZilskeLameckerZachow2007, author = {Zilske, Michael and Lamecker, Hans and Zachow, Stefan}, title = {Adaptive Remeshing of Non-Manifold Surfaces}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-9445}, number = {07-01}, year = {2007}, abstract = {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.}, language = {en} } @misc{EhlkeRammLameckeretal.2013, author = {Ehlke, Moritz and Ramm, Heiko and Lamecker, Hans and Hege, Hans-Christian and Zachow, Stefan}, title = {Fast Generation of Virtual X-ray Images from Deformable Tetrahedral Meshes}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-41896}, year = {2013}, abstract = {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.}, language = {en} } @misc{EhlkeRammLameckeretal.2012, author = {Ehlke, Moritz and Ramm, Heiko and Lamecker, Hans and Zachow, Stefan}, title = {Efficient projection and deformation of volumetric intensity models for accurate simulation of X-ray images}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-16580}, year = {2012}, abstract = {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.}, language = {en} } @misc{RammMorilloVictoriaTodtetal.2013, author = {Ramm, Heiko and Morillo Victoria, Oscar Salvador and Todt, Ingo and Schirmacher, Hartmut and Ernst, Arneborg and Zachow, Stefan and Lamecker, Hans}, title = {Visual Support for Positioning Hearing Implants}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-42495}, year = {2013}, abstract = {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.}, language = {en} } @misc{AmbellanLameckervonTycowiczetal.2019, author = {Ambellan, Felix and Lamecker, Hans and von Tycowicz, Christoph and Zachow, Stefan}, title = {Statistical Shape Models - Understanding and Mastering Variation in Anatomy}, issn = {1438-0064}, doi = {10.1007/978-3-030-19385-0_5}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-72699}, year = {2019}, abstract = {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.}, language = {en} } @misc{AmbellanTackEhlkeetal.2019, author = {Ambellan, Felix and Tack, Alexander and Ehlke, Moritz and Zachow, Stefan}, title = {Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-72704}, year = {2019}, abstract = {We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs).The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures.The shape models and neural networks employed are trained using data from the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets from the SKI10 challenge.For the first time, an accuracy equivalent to the inter-observer variability of human readers is achieved in this challenge.Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We make the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation.In conclusion, combining localized classification via CNNs with statistical anatomical knowledge via SSMs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data.}, language = {en} } @misc{TackZachow2019, author = {Tack, Alexander and Zachow, Stefan}, title = {Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-71439}, year = {2019}, abstract = {Volumetry of the cartilage of the knee, as needed for the assessment of knee osteoarthritis (KOA), is typically performed in a tedious and subjective process. We present an automated segmentation-based method for the quantification of cartilage volume by employing 3D Convolutional Neural Networks (CNNs). CNNs were trained in a supervised manner using magnetic resonance imaging data as well as cartilage volumetry readings given by clinical experts for 1378 subjects. It was shown that 3D CNNs can be employed for cartilage volumetry with an accuracy similar to expert volumetry readings. In future, accurate automated cartilage volumetry might support both, diagnosis of KOA as well as assessment of KOA progression via longitudinal analysis.}, language = {en} } @misc{TycowiczAmbellanMukhopadhyayetal.2016, author = {Tycowicz, Christoph von and Ambellan, Felix and Mukhopadhyay, Anirban and Zachow, Stefan}, title = {A Riemannian Statistical Shape Model using Differential Coordinates}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-61175}, year = {2016}, abstract = {We propose a novel Riemannian framework for statistical analysis of shapes that is able to account for the nonlinearity in shape variation. By adopting a physical perspective, we introduce a differential representation that puts the local geometric variability into focus. We model these differential coordinates as elements of a Lie group thereby endowing our shape space with a non-Euclidian structure. A key advantage of our framework is that statistics in a manifold shape space become numerically tractable improving performance by several orders of magnitude over state-of-the-art. We show that our Riemannian model is well suited for the identification of intra-population variability as well as inter-population differences. In particular, we demonstrate the superiority of the proposed model in experiments on specificity and generalization ability. We further derive a statistical shape descriptor that outperforms the standard Euclidian approach in terms of shape-based classification of morphological disorders.}, language = {en} } @inproceedings{GreweZachow2016, author = {Grewe, Carl Martin and Zachow, Stefan}, title = {Fully Automated and Highly Accurate Dense Correspondence for Facial Surfaces}, volume = {9914}, booktitle = {Computer Vision - ECCV 2016 Workshops}, publisher = {Springer International Publishing}, doi = {10.1007/978-3-319-48881-3_38}, pages = {552 -- 568}, year = {2016}, abstract = {We present a novel framework for fully automated and highly accurate determination of facial landmarks and dense correspondence, e.g. a topologically identical mesh of arbitrary resolution, across the entire surface of 3D face models. For robustness and reliability of the proposed approach, we are combining 2D landmark detectors and 3D statistical shape priors with a variational matching method. Instead of matching faces in the spatial domain only, we employ image registration to align the 2D parametrization of the facial surface to a planar template we call the Unified Facial Parameter Domain (ufpd). This allows us to simultaneously match salient photometric and geometric facial features using robust image similarity measures while reasonably constraining geometric distortion in regions with less significant features. We demonstrate the accuracy of the dense correspondence established by our framework on the BU3DFE database with 2500 facial surfaces and show, that our framework outperforms current state-of-the-art methods with respect to the fully automated location of facial landmarks.}, language = {en} }