Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields

R. Trullo, C. Petitjean, S. Ruan, B. Dubray, D. Nie, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Citations (Scopus)

Abstract

Cancer is one of the leading causes of death worldwide. Radiotherapy is a standard treatment for this condition and the first step of the radiotherapy process is to identify the target volumes to be targeted and the healthy organs at risk (OAR) to be protected. Unlike previous methods for automatic segmentation of OAR that typically use local information and individually segment each OAR, in this paper, we propose a deep learning framework for the joint segmentation of OAR in CT images of the thorax, specifically the heart, esophagus, trachea and the aorta. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Finally, by using Conditional Random Fields (specifically the CRF as Recurrent Neural Network model), we are able to account for relationships between the organs to further improve the segmentation results. Experiments demonstrate competitive performance on a dataset of 30 CT scans.

Original languageEnglish
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PublisherIEEE Computer Society
Pages1003-1006
Number of pages4
ISBN (Electronic)9781509011711
DOIs
Publication statusPublished - 2017 Jun 15
Externally publishedYes
Event14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia
Duration: 2017 Apr 182017 Apr 21

Other

Other14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
CountryAustralia
CityMelbourne
Period17/4/1817/4/21

    Fingerprint

Keywords

  • CRF
  • CRFasRNN
  • CT Segmentation
  • Fully Convolutional Networks (FCN)

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Trullo, R., Petitjean, C., Ruan, S., Dubray, B., Nie, D., & Shen, D. (2017). Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 (pp. 1003-1006). [7950685] IEEE Computer Society. https://doi.org/10.1109/ISBI.2017.7950685