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Segmentation of multiple structures in chest radiographs using multi-task fully convolutional networks
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
2017 (English)In: 20th Scandinavian Conference on Image Analysis, SCIA 2017, Springer, 2017, Vol. 10270, 282-289 p.Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10270, 282-289 p.
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10270
Keyword [en]
Fully convolutional network, Image segmentation, Multi-task deep neural network
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-210000DOI: 10.1007/978-3-319-59129-2_24Scopus ID: 2-s2.0-85020475191ISBN: 9783319591285 (print)OAI: oai:DiVA.org:kth-210000DiVA: diva2:1116937
Conference
20th Scandinavian Conference on Image Analysis, SCIA 2017, Tromso, Norway, 12 June 2017 through 14 June 2017
Note

QC 20170628

Available from: 2017-06-28 Created: 2017-06-28 Last updated: 2017-06-28Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf