CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation

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

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


Accurate segmentation of the prostate and organs at risk (e.g., bladder and rectum) in CT images is a crucial step for radiation therapy in the treatment of prostate cancer. However, it is a very challenging task due to unclear boundaries, large intra- and inter-patient shape variability, and uncertain existence of bowel gases and fiducial markers. In this paper, we propose a novel automatic segmentation framework using fully convolutional networks with boundary sensitive representation to address this challenging problem. Our novel segmentation framework contains three modules. First, an organ localization model is designed to focus on the candidate segmentation region of each organ for better performance. Then, a boundary sensitive representation model based on multi-task learning is proposed to represent the semantic boundary information in a more robust and accurate manner. Finally, a multi-label cross-entropy loss function combining boundary sensitive representation is introduced to train a fully convolutional network for the organ segmentation. The proposed method is evaluated on a large and diverse planning CT dataset with 313 images from 313 prostate cancer patients. Experimental results show that the performance of our proposed method outperforms the baseline fully convolutional networks, as well as other state-of-the-art methods in CT male pelvic organ segmentation.

Original languageEnglish
Pages (from-to)168-178
Number of pages11
JournalMedical Image Analysis
Publication statusPublished - 2019 May


  • Boundary sensitive
  • CT
  • Fully convolutional network
  • Image segmentation
  • Male pelvic organ
  • Prostate cancer

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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