Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Automatic whole heart segmentation using deep learning and shape context
KTH, Skolan för teknik och hälsa (STH).ORCID-id: 0000-0002-0442-3524
KTH, Skolan för teknik och hälsa (STH).ORCID-id: 0000-0002-7750-1917
2018 (engelsk)Inngår i: 8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017, Springer, 2018, Vol. 10663, s. 242-249Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer, 2018. Vol. 10663, s. 242-249
Serie
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10663
Emneord [en]
Deep learning, Fully convolutional network, Heart segmentation, Shape context, Statistic shape model
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-225494DOI: 10.1007/978-3-319-75541-0_26Scopus ID: 2-s2.0-85044467877ISBN: 9783319755403 (tryckt)OAI: oai:DiVA.org:kth-225494DiVA, id: diva2:1195738
Konferanse
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
Forskningsfinansiär
Swedish Heart Lung Foundation, 2016-0609Swedish Research Council, 2014-6153
Merknad

QC 20180406

Tilgjengelig fra: 2018-04-06 Laget: 2018-04-06 Sist oppdatert: 2018-04-06bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Personposter BETA

Wang, ChunliangSmedby, Örjan

Søk i DiVA

Av forfatter/redaktør
Wang, ChunliangSmedby, Örjan
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 812 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf