Hierarchical representation for CT prostate segmentation

Shuai Wang, Kelei He, Dong Nie, Sihang Zhou, Yaozong Gao, Dinggang Shen

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

2 Citations (Scopus)


Traditional approaches for automatic CT prostate segmentation often guide feature representation learning directly based on manual delineation to deal with this challenging task (due to unclear boundaries and large shape variations), which does not fully exploit the prior information and leads to insufficient discriminability. In this paper, we propose a novel hierarchical representation learning method to segment the prostate in CT images. Specifically, one multi-task model under the supervision of a series of morphological masks transformed from the manual delineation aims to generate hierarchical feature representations for the prostate. Then, leveraging both these generated rich representations and intensity images, one fully convolutional network (FCN) carries out the accurate segmentation of the prostate. To evaluate the performance, a large and challenging CT dataset is adopted, and the experimental results show our method achieves significant improvement compared with conventional FCNs.

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781538636411
Publication statusPublished - 2019 Apr
Externally publishedYes
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 2019 Apr 82019 Apr 11

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019


  • CT
  • Feature representation
  • Fully convolutional network (FCN)
  • Image segmentation
  • Prostate

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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