Unpaired Dual-Modal Image Complementation Learning for Single-Modal Medical Image Segmentation.

Journal: IEEE transactions on bio-medical engineering
Published Date:

Abstract

OBJECTIVE: Multi-modal MR/CT image segmentation is an important task in disease diagnosis and treatment, but it is usually difficult to acquire aligned multi-modal images of a patient in clinical practice due to the high cost and specific allergic reactions to contrast agents. To address these issues, a task complementation framework is proposed to enable unpaired multi-modal image complementation learning in the training stage and single-modal image segmentation in the inference stage.

Authors

  • Dehui Xiang
  • Tao Peng
    Department of Biology, Shantou University, Shantou, China.
  • Yun Bian
    Department of Radiology, Changhai Hospital.
  • Lang Chen
    University of Wisconsin-Madison.
  • Jianbin Zeng
  • Fei Shi
    Medical Image Processing, Analysis, and Visualization (MIVAP) Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, China.
  • Weifang Zhu
  • Xinjian Chen
    Medical Image Processing, Analysis, and Visualization (MIVAP) Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, China.