Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images.

Journal: BMC systems biology
Published Date:

Abstract

BACKGROUND: Efficient computational recognition and segmentation of target organ from medical images are foundational in diagnosis and treatment, especially about pancreas cancer. In practice, the diversity in appearance of pancreas and organs in abdomen, makes detailed texture information of objects important in segmentation algorithm. According to our observations, however, the structures of previous networks, such as the Richer Feature Convolutional Network (RCF), are too coarse to segment the object (pancreas) accurately, especially the edge.

Authors

  • Min Fu
    Mathematics Department, School of Information, Renmin University of China, Beijing, China.
  • Wenming Wu
    Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Xiafei Hong
    Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Qiuhua Liu
    Mathematics Department, School of Information, Renmin University of China, Beijing, China.
  • Jialin Jiang
    Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Yaobin Ou
    Mathematics Department, School of Information, Renmin University of China, Beijing, China. ou@ruc.edu.cn.
  • Yupei Zhao
  • Xinqi Gong