Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (2): 7-11.doi: 10.16180/j.cnki.issn1007-7820.2021.02.002

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Research Progress of Medical Image Segmentation Based on Deep Learning

YAN Chao,SUN Zhanquan,TIAN Engang,ZHAO Yangyang,FAN Xiaoyan   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2019-12-03 Online:2021-02-15 Published:2021-01-22
  • Supported by:
    National Natural Science Foundation of China(61773218)


Medical image segmentation plays an important role in clinical diagnosis and is the basis of other medical image processing methods. With the improvement of computer hardware performance, image segmentation technology based on deep learning has already become a powerful tool for processing medical images and is widely used in various medical image segmentation tasks. This paper introduces several types of common medical images and their characteristics, analyzes and compares the image segmentation algorithms that have emerged in recent years. Some algorithms have been successfully applied to segmentation tasks such as brain tissue, lungs and blood vessels. In response to the current problems in the development of medical image segmentation technology based on deep learning, corresponding strategies are proposed, and the future development direction is also prospected.

Key words: deep learning, medical image, neural network, convolution operation, segmentation algorithm, image processing

CLC Number: 

  • TP391.41