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Automatic whole heart segmentation using deep learning and shape context
KTH, School of Technology and Health (STH).ORCID iD: 0000-0002-0442-3524
KTH, School of Technology and Health (STH).ORCID iD: 0000-0002-7750-1917
2018 (English)In: 8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017, Springer, 2018, Vol. 10663, p. 242-249Conference paper, Published paper (Refereed)
Abstract [en]

To assist 3D cardiac image analysis, we propose an automatic whole heart segmentation using a deep learning framework combined with shape context information that is encoded in volumetric shape models. The proposed processing pipeline consists of three major steps: scout segmentation with orthogonal 2D U-nets, shape context estimation and refining segmentation with U-net and shape context. The proposed method was evaluated using the MMWHS challenge data. Two sets of networks were trained separately for contrast-enhanced CT and MRI. On the 20 training datasets, using 5-fold cross-validation, the average Dice coefficients for the left ventricle, the right ventricle, the left atrium, the right atrium and the myocardium of the left ventricle were 0.895, 0.795, 0.847, 0.821, 0.807 for MRI and 0.935, 0.825, 0.908, 0.881, 0.879 for CT, respectively. Further improvement may be possible given more training data or advanced data augmentation strategy.

Place, publisher, year, edition, pages
Springer, 2018. Vol. 10663, p. 242-249
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10663
Keywords [en]
Deep learning, Fully convolutional network, Heart segmentation, Shape context, Statistic shape model
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:kth:diva-225494DOI: 10.1007/978-3-319-75541-0_26Scopus ID: 2-s2.0-85044467877ISBN: 9783319755403 OAI: oai:DiVA.org:kth-225494DiVA, id: diva2:1195738
Conference
8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, 10 September 2017 through 14 September 2017
Funder
Swedish Heart Lung Foundation, 2016-0609Swedish Research Council, 2014-6153
Note

QC 20180406

Available from: 2018-04-06 Created: 2018-04-06 Last updated: 2018-04-06Bibliographically approved

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Wang, ChunliangSmedby, Örjan

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CiteExportLink to record
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Citation style
  • apa
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  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
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  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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