Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • 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
Segmentation of multiple structures in chest radiographs using multi-task fully convolutional networks
KTH, Skolan för teknik och hälsa (STH), Medicinsk teknik, Medicinsk bildbehandling och visualisering.
2017 (engelsk)Inngår i: 20th Scandinavian Conference on Image Analysis, SCIA 2017, Springer, 2017, Vol. 10270, s. 282-289Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Segmentation of various structures from the chest radiograph is often performed as an initial step in computer-aided diagnosis/detection (CAD) systems. In this study, we implemented a multi-task fully convolutional network (FCN) to simultaneously segment multiple anatomical structures, namely the lung fields, the heart, and the clavicles, in standard posterior-anterior chest radiographs. This is done by adding multiple fully connected output nodes on top of a single FCN and using different objective functions for different structures, rather than training multiple FCNs or using a single FCN with a combined objective function for multiple classes. In our preliminary experiments, we found that the proposed multi-task FCN can not only reduce the training and running time compared to treating the multi-structure segmentation problems separately, but also help the deep neural network to converge faster and deliver better segmentation results on some challenging structures, like the clavicle. The proposed method was tested on a public database of 247 posterior–anterior chest radiograph and achieved comparable or higher accuracy on most of the structures when compared with the state-of-the-art segmentation methods.

sted, utgiver, år, opplag, sider
Springer, 2017. Vol. 10270, s. 282-289
Serie
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10270
Emneord [en]
Fully convolutional network, Image segmentation, Multi-task deep neural network
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-210000DOI: 10.1007/978-3-319-59129-2_24ISI: 000454360300024Scopus ID: 2-s2.0-85020475191ISBN: 9783319591285 (tryckt)OAI: oai:DiVA.org:kth-210000DiVA, id: diva2:1116937
Konferanse
20th Scandinavian Conference on Image Analysis, SCIA 2017, Tromso, Norway, 12 June 2017 through 14 June 2017
Merknad

QC 20170628

Tilgjengelig fra: 2017-06-28 Laget: 2017-06-28 Sist oppdatert: 2019-09-18bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Søk i DiVA

Av forfatter/redaktør
Wang, Chunliang
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 208 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • 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