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Multimedia Contribution
DOI: 10.4230/LIPIcs.SoCG.2017.65
Cardiac Trabeculae Segmentation: an Application of Computational Topology (Multimedia Contribution)

Authors: Chao Chen, Dimitris Metaxas, Yusu Wang, and Pengxiang Wu

Published in: LIPIcs, Volume 77, 33rd International Symposium on Computational Geometry (SoCG 2017)

Abstract
In this video, we present a research project on cardiac trabeculae segmentation. Trabeculae are fine muscle columns within human ventricles whose both ends are attached to the wall. Extracting these structures are very challenging even with state-of-the-art image segmentation techniques. We observed that these structures form natural topological handles. Based on such observation, we developed a topological approach, which employs advanced computational topology methods and achieve high quality segmentation results.
Cite as

Chao Chen, Dimitris Metaxas, Yusu Wang, and Pengxiang Wu. Cardiac Trabeculae Segmentation: an Application of Computational Topology (Multimedia Contribution). In 33rd International Symposium on Computational Geometry (SoCG 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 77, pp. 65:1-65:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{chen_et_al:LIPIcs.SoCG.2017.65,
  author =	{Chen, Chao and Metaxas, Dimitris and Wang, Yusu and Wu, Pengxiang},
  title =	{{Cardiac Trabeculae Segmentation: an Application of Computational Topology}},
  booktitle =	{33rd International Symposium on Computational Geometry (SoCG 2017)},
  pages =	{65:1--65:4},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-038-5},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{77},
  editor =	{Aronov, Boris and Katz, Matthew J.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2017.65},
  URN =		{urn:nbn:de:0030-drops-72429},
  doi =		{10.4230/LIPIcs.SoCG.2017.65},
  annote =	{Keywords: image segmentation, trabeculae, persistent homology, homology localization}
}
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