CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation

Accurate and automatic segmentation of medical images is a crucial step for clinical diagnosis and analysis. The convolutional neural network (CNN) approaches based on the U-shape structure have achieved remarkable performances in many different medical image segmentation tasks. However, the context information extraction capability of single stage is insufficient in this structure, due to the problems such as imbalanced class and blurred boundary. In this paper, we propose a novel Context Pyramid Fusion Network (named CPFNet) by combining two pyramidal modules to fuse global/multi-scale context information. Based on the U-shape structure, we first design multiple global pyramid guidance (GPG) modules between…

• NN
  National Natural Science Foundation of China

  Awards: 81401472, 61622114
• NK
  National Key Research and Development Program of China

  Award: 2014CB748600