An effective MR-Guided CT network training for segmenting prostate in CT images

Wanqi Yang, Yinghuan Shi, Sang Hyun Park, Ming Yang, Yang Gao, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Segmentation of prostate in medical imaging data (e.g., CT, MRI, TRUS) is often considered as a critical yet challenging task for radiotherapy treatment. It is relatively easier to segment prostate from MR images than from CT images, due to better soft tissue contrast of the MR images. For segmenting prostate from CT images, most previous methods mainly used CT alone, and thus their performances are often limited by low tissue contrast in the CT images. In this article, we explore the possibility of using indirect guidance from MR images for improving prostate segmentation in the CT images. In particular, we propose a novel deep transfer learning approach, i.e., MR-guided CT network training (namely MICS-NET), which can employ MR images to help better learning of features in CT images for prostate segmentation. In MICS-NET, the guidance from MRI consists of two steps: (1) learning informative and transferable features from MRI and then transferring them to CT images in a cascade manner, and (2) adaptively transferring the prostate likelihood of MRI model (i.e., well-trained convnet by purely using MR images) with a view consistency constraint. To illustrate the effectiveness of our approach, we evaluate MICS-NET on a real CT prostate image set, with the manual delineations available as the ground truth for evaluation. Our methods generate promising segmentation results which achieve (1) six percentages higher Dice Ratio than the CT model purely using CT images and (2) comparable performance with the MRI model purely using MR images.

Original languageEnglish
Article number8933421
Pages (from-to)2278-2291
Number of pages14
JournalIEEE Journal of Biomedical and Health Informatics
Issue number8
Publication statusPublished - 2020 Aug

Bibliographical note

Funding Information:
Manuscript received June 25, 2019; revised December 3, 2019; accepted December 12, 2019. Date of publication December 16, 2019; date of current version August 5, 2020. This work was supported in part by the National Natural Science Foundation of China under Grants 61603193, 61673203, 61876087, and 61432008, in part by the Jiangsu Natural Science Foundation under Grant BK20171479, in part by the Fundamental Research Funds for the Central Universities under Grant 020214380056, and in part by the Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korea government (2019-0-01557). (Corresponding authors: Wanqi Yang; Ding-gang Shen.) W. Yang and M. Yang are with the School of Computer Science and Technology, Nanjing Normal University, Nanjing 210046 China (e-mail:;

Publisher Copyright:
© 2013 IEEE.


  • Prostate segmentation
  • cascade learning
  • deep transfer learning
  • fully convolutional network
  • view consistency constraint

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics
  • Electrical and Electronic Engineering
  • Computer Science Applications


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