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    <title language="eng">Statistical Shape Models - Understanding and Mastering Variation in Anatomy</title>
    <abstract language="eng">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.</abstract>
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    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Hans Lamecker</author>
    <author>Christoph von Tycowicz</author>
    <author>Stefan Zachow</author>
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  <doc>
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    <publishedYear>2019</publishedYear>
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    <title language="eng">Statistical Shape Models - Understanding and Mastering Variation in Anatomy</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Biomedical Visualisation</parentTitle>
    <identifier type="isbn">978-3-030-19384-3</identifier>
    <identifier type="doi">10.1007/978-3-030-19385-0_5</identifier>
    <identifier type="isbn">978-3-030-19385-0</identifier>
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    <author>Felix Ambellan</author>
    <submitter>Stefan Zachow</submitter>
    <editor>Paul M. Rea</editor>
    <author>Hans Lamecker</author>
    <author>Christoph von Tycowicz</author>
    <author>Stefan Zachow</author>
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    <title language="eng">An as-invariant-as-possible GL+(3)-based Statistical Shape Model</title>
    <abstract language="eng">We describe a novel nonlinear statistical shape model basedon differential coordinates viewed as elements of GL+(3). We adopt an as-invariant-as possible framework comprising a bi-invariant Lie group mean and a tangent principal component analysis based on a unique GL+(3)-left-invariant, O(3)-right-invariant metric. Contrary to earlier work that equips the coordinates with a specifically constructed group structure, our method employs the inherent geometric structure of the group-valued data and therefore features an improved statistical power in identifying shape differences. We demonstrate this in experiments on two anatomical datasets including comparison to the standard Euclidean as well as recent state-of-the-art nonlinear approaches to statistical shape modeling.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-74566</identifier>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Stefan Zachow</author>
    <author>Christoph von Tycowicz</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-46</number>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Statistical shape analysis</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Tangent principal component analysis</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Lie groups</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Manifold valued statistics</value>
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    <title language="eng">An as-invariant-as-possible GL+(3)-based Statistical Shape Model</title>
    <abstract language="eng">We describe a novel nonlinear statistical shape model basedon differential coordinates viewed as elements of GL+(3). We adopt an as-invariant-as possible framework comprising a bi-invariant Lie group mean and a tangent principal component analysis based on a unique GL+(3)-left-invariant, O(3)-right-invariant metric. Contrary to earlier work that equips the coordinates with a specifically constructed group structure, our method employs the inherent geometric structure of the group-valued data and therefore features an improved statistical power in identifying shape differences. We demonstrate this in experiments on two anatomical datasets including comparison to the standard Euclidean as well as recent state-of-the-art nonlinear approaches to statistical shape modeling.</abstract>
    <parentTitle language="eng">Proc. 7th MICCAI workshop on Mathematical Foundations of Computational Anatomy (MFCA)</parentTitle>
    <identifier type="doi">10.1007/978-3-030-33226-6_23</identifier>
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    <enrichment key="AcceptedDate">2019-08-19</enrichment>
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    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Stefan Zachow</author>
    <author>Christoph von Tycowicz</author>
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    <title language="eng">A Surface-Theoretic Approach for Statistical Shape Modeling</title>
    <abstract language="eng">We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. We evaluate the performance of our model w.r.t. shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing natural biological shape variability, we carry out an analysis of specificity and generalization ability.</abstract>
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    <identifier type="urn">urn:nbn:de:0297-zib-74497</identifier>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Stefan Zachow</author>
    <author>Christoph von Tycowicz</author>
    <series>
      <title>ZIB-Report</title>
      <number>19-20</number>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Statistical shape analysis</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Principal geodesic analysis</value>
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      <type>uncontrolled</type>
      <value>Lie groups</value>
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      <type>uncontrolled</type>
      <value>Classification</value>
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      <value>Manifold valued statistics</value>
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    <title language="eng">A Surface-Theoretic Approach for Statistical Shape Modeling</title>
    <abstract language="eng">We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. We evaluate the performance of our model w.r.t. shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model’s ability of capturing natural biological shape variability, we carry out an analysis of specificity and generalization ability.</abstract>
    <parentTitle language="eng">Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part IV</parentTitle>
    <identifier type="doi">10.1007/978-3-030-32251-9_3</identifier>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
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    <enrichment key="AcceptedDate">2019-06-05</enrichment>
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    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Stefan Zachow</author>
    <author>Christoph von Tycowicz</author>
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    <title language="eng">VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images</title>
    <parentTitle language="eng">arXiv</parentTitle>
    <identifier type="arxiv">2001.09193</identifier>
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    <enrichment key="FulltextUrl">https://arxiv.org/pdf/2001.09193.pdf</enrichment>
    <submitter>Tamaz Amiranashvili</submitter>
    <author>Anjany Sekuboyina</author>
    <author>Amirhossein Bayat</author>
    <author>Malek E. Husseini</author>
    <author>Maximilian Löffler</author>
    <author>Hongwei Li</author>
    <author>Giles Tetteh</author>
    <author>Jan Kukačka</author>
    <author>Christian Payer</author>
    <author>Darko Štern</author>
    <author>Martin Urschler</author>
    <author>Maodong Chen</author>
    <author>Dalong Cheng</author>
    <author>Nikolas Lessmann</author>
    <author>Yujin Hu</author>
    <author>Tianfu Wang</author>
    <author>Dong Yang</author>
    <author>Daguang Xu</author>
    <author>Felix Ambellan</author>
    <author>Tamaz Amiranashvili</author>
    <author>Moritz Ehlke</author>
    <author>Hans Lamecker</author>
    <author>Sebastian Lehnert</author>
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    <author>Nicolás Pérez de Olaguer</author>
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    <author>Manish Sahu</author>
    <author>Alexander Tack</author>
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    <author>Tao Jiang</author>
    <author>Xinjun Ma</author>
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    <author>Xin Wang</author>
    <author>Qingyue Wei</author>
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    <author>Matthias Wolf</author>
    <author>Alexandre Kirszenberg</author>
    <author>Élodie Puybareau</author>
    <author>Alexander Valentinitsch</author>
    <author>Markus Rempfler</author>
    <author>Björn H. Menze</author>
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    <collection role="persons" number="ehlke">Ehlke, Moritz</collection>
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    <language>eng</language>
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    <title language="eng">Rigid Motion Invariant Statistical Shape Modeling based on Discrete Fundamental Forms</title>
    <abstract language="eng">We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. Additionally, as planar configurations form a submanifold in shape space, our representation allows for effective estimation of quasi-isometric surfaces flattenings. We evaluate the performance of our model w.r.t. shape-based classification of hippocampus and femur malformations due to Alzheimer's disease and osteoarthritis, respectively. In particular, we achieve state-of-the-art accuracies outperforming the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing biological shape variability, we carry out an analysis of specificity and generalization ability.</abstract>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="doi">10.1016/j.media.2021.102178</identifier>
    <identifier type="arxiv">2111.06850</identifier>
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    <enrichment key="AcceptedDate">2021/07/13</enrichment>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Stefan Zachow</author>
    <author>Christoph von Tycowicz</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="persons" number="vontycowicz">Tycowicz, Christoph von</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MathPlus-TrU-1">MathPlus-TrU-1</collection>
    <collection role="projects" number="MathPlus-EF2-3">MathPlus-EF2-3</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="institutes" number="GDAP">Geometric Data Analysis and Processing</collection>
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  <doc>
    <id>8154</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>177</pageFirst>
    <pageLast>188</pageLast>
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    <issue/>
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    <publisherName/>
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    <completedDate>2021-06-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Geodesic B-Score for Improved Assessment of Knee Osteoarthritis</title>
    <abstract language="eng">Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination ability over its Euclidean counterpart, which we demonstrate for predictive validity on assessing risks of total knee replacement. This result highlights the potential of the geodesic B-score to enable improved personalized assessment and stratification for interventions.</abstract>
    <parentTitle language="eng">Proc. Information Processing in Medical Imaging (IPMI)</parentTitle>
    <identifier type="arxiv">2104.01107</identifier>
    <identifier type="doi">10.1007/978-3-030-78191-0_14</identifier>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2021-02-12</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-81930</enrichment>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Stefan Zachow</author>
    <author>Christoph von Tycowicz</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="persons" number="vontycowicz">Tycowicz, Christoph von</collection>
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  <doc>
    <id>8339</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>73</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="doi">10.1016/j.media.2021.102166</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">06.07.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Anjany Sekuboyina</author>
    <submitter>Tamaz Amiranashvili</submitter>
    <author>Malek E. Husseini</author>
    <author>Amirhossein Bayat</author>
    <author>Maximilian Löffler</author>
    <author>Hans Liebl</author>
    <author>Hongwei Li</author>
    <author>Giles Tetteh</author>
    <author>Jan Kukačka</author>
    <author>Christian Payer</author>
    <author>Darko Štern</author>
    <author>Martin Urschler</author>
    <author>Maodong Chen</author>
    <author>Dalong Cheng</author>
    <author>Nikolas Lessmann</author>
    <author>Yujin Hu</author>
    <author>Tianfu Wang</author>
    <author>Dong Yang</author>
    <author>Daguang Xu</author>
    <author>Felix Ambellan</author>
    <author>Tamaz Amiranashvili</author>
    <author>Moritz Ehlke</author>
    <author>Hans Lamecker</author>
    <author>Sebastian Lehnert</author>
    <author>Marilia Lirio</author>
    <author>Nicolás Pérez de Olaguer</author>
    <author>Heiko Ramm</author>
    <author>Manish Sahu</author>
    <author>Alexander Tack</author>
    <author>Stefan Zachow</author>
    <author>Tao Jiang</author>
    <author>Xinjun Ma</author>
    <author>Christoph Angerman</author>
    <author>Xin Wang</author>
    <author>Kevin Brown</author>
    <author>Alexandre Kirszenberg</author>
    <author>Élodie Puybareau</author>
    <author>Di Chen</author>
    <author>Yiwei Bai</author>
    <author>Brandon H. Rapazzo</author>
    <author>Timyoas Yeah</author>
    <author>Amber Zhang</author>
    <author>Shangliang Xu</author>
    <author>Feng Hou</author>
    <author>Zhiqiang He</author>
    <author>Chan Zeng</author>
    <author>Zheng Xiangshang</author>
    <author>Xu Liming</author>
    <author>Tucker J. Netherton</author>
    <author>Raymond P. Mumme</author>
    <author>Laurence E. Court</author>
    <author>Zixun Huang</author>
    <author>Chenhang He</author>
    <author>Li-Wen Wang</author>
    <author>Sai Ho Ling</author>
    <author>Lê Duy Huynh</author>
    <author>Nicolas Boutry</author>
    <author>Roman Jakubicek</author>
    <author>Jiri Chmelik</author>
    <author>Supriti Mulay</author>
    <author>Mohanasankar Sivaprakasam</author>
    <author>Johannes C. Paetzold</author>
    <author>Suprosanna Shit</author>
    <author>Ivan Ezhov</author>
    <author>Benedikt Wiestler</author>
    <author>Ben Glocker</author>
    <author>Alexander Valentinitsch</author>
    <author>Markus Rempfler</author>
    <author>Björn H. Menze</author>
    <author>Jan S. Kirschke</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="projects" number="MathPlus-TrU-1">MathPlus-TrU-1</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>8193</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2021-03-29</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Geodesic B-Score for Improved Assessment of Knee Osteoarthritis</title>
    <abstract language="eng">Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination ability over its Euclidean counterpart, which we demonstrate for predictive validity on assessing risks of total knee replacement. This result highlights the potential of the geodesic B-score to enable improved personalized assessment and stratification for interventions.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-81930</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <author>Felix Ambellan</author>
    <submitter>Felix Ambellan</submitter>
    <author>Stefan Zachow</author>
    <author>Christoph von Tycowicz</author>
    <series>
      <title>ZIB-Report</title>
      <number>21-09</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Statistical shape analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Osteoarthritis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Geometric statistics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Riemannian manifolds</value>
    </subject>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="persons" number="vontycowicz">Tycowicz, Christoph von</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MathPlus-TrU-1">MathPlus-TrU-1</collection>
    <collection role="projects" number="BIFOLD">BIFOLD</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/8193/ZIBReport_21-09.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/8193/ZIBReport_21-09_suppl.zip</file>
  </doc>
  <doc>
    <id>8721</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>13432</volume>
    <type>conferenceobject</type>
    <publisherName>Springer, Cham</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Landmark-free Statistical Shape Modeling via Neural Flow Deformations</title>
    <abstract language="eng">Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shape reconstruction and image segmentation, but also shape generation and classification. Existing shape priors either require dense correspondence between training examples or lack robustness and topological guarantees. We present FlowSSM, a novel shape modeling approach that learns shape variability without requiring dense correspondence between training instances. It relies on a hierarchy of continuous deformation flows, which are parametrized by a neural network. Our model outperforms state-of-the-art methods in providing an expressive and robust shape prior for distal femur and liver. We show that the emerging latent representation is discriminative by separating healthy from pathological shapes. Ultimately, we demonstrate its effectiveness on two shape reconstruction tasks from partial data. Our source code is publicly available (https://github.com/davecasp/flowssm).</abstract>
    <parentTitle language="eng">Medical Image Computing and Computer Assisted Intervention - MICCAI 2022</parentTitle>
    <identifier type="doi">10.1007/978-3-031-16434-7_44</identifier>
    <identifier type="arxiv">2209.06861</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2022-06-01</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Series">Lecture Notes in Computer Science</enrichment>
    <author>David Lüdke</author>
    <submitter>David Lüdke</submitter>
    <author>Tamaz Amiranashvili</author>
    <author>Felix Ambellan</author>
    <author>Ivan Ezhov</author>
    <author>Bjoern Menze</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
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