CE-Net: Context Encoder Network for 2D Medical Image Segmentation

Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, and so on. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations led to the loss of some spatial information. In this paper, we propose a context encoder network (CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation. CE-Net mainly contains three major components: a feature…

• NN
  National Natural Science Foundation of China

  Award: 61601029
• CA
  Chinese Academy of Sciences

  Awards: Y60001RA01, Y80002RA01
• NS
  Natural Science Foundation of Zhejiang Province

  Award: LZ19F010001