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Presentation
A Novel Image-Specific Transfer Approach for Prostate Segmentation in MR Images
Faculty of Engineering and Information Sciences - Papers: Part B
  • Pinzhuo Tian, Nanjing University
  • Lei Qi, Nanjing University
  • Yinghuan Shi, Nanjing University
  • Luping Zhou, University of Wollongong
  • Yang Gao, Nanjing University
  • Dinggang Sheri, University of North Carolina
RIS ID
130862
Publication Date
1-1-2018
Publication Details
Tian, P., Qi, L., Shi, Y., Zhou, L., Gao, Y. & Sheri, D. (2018). A Novel Image-Specific Transfer Approach for Prostate Segmentation in MR Images. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 806-810). United States: IEEE.
Abstract

Prostate segmentation in Magnetic Resonance (MR) Images is a significant yet challenging task for prostate cancer treatment. Most of the existing works attempted to design a global classifier for all MR images, which neglect the discrepancy of images across different patients. To this end, we propose a novel transfer approach for prostate segmentation in MR images. Firstly, an image-specific classifier is built for each training image. Secondly, a pair of dictionaries and a mapping matrix are jointly obtained by a novel Semi-Coupled Dictionary Transfer Learning (SCDTL). Finally, the classifiers on the source domain could be selectively transferred to the target domain (i.e. testing images) by the dictionaries and the mapping matrix. The evaluation demonstrates that our approach has a competitive performance compared with the state-of-the-art transfer learning methods. Moreover, the proposed transfer approach outperforms the conventional deep neural network based method.

Grant Number
ARC/DE160100241
Citation Information
Pinzhuo Tian, Lei Qi, Yinghuan Shi, Luping Zhou, et al.. "A Novel Image-Specific Transfer Approach for Prostate Segmentation in MR Images" (2018)
Available at: http://works.bepress.com/lei-qi/5/