From CNN to Transformer: A Review of Medical Image Segmentation Models.

Journal: Journal of imaging informatics in medicine
Published Date: Mar 4, 2024

Abstract

Medical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and treatment. The use of deep learning for image segmentation has become a prevalent trend. The widely adopted approach currently is U-Net and its variants. Moreover, with the remarkable success of pre-trained models in natural language processing tasks, transformer-based models like TransUNet have achieved desirable performance on multiple medical image segmentation datasets. Recently, the Segment Anything Model (SAM) and its variants have also been attempted for medical image segmentation. In this paper, we conduct a survey of the most representative seven medical image segmentation models in recent years. We theoretically analyze the characteristics of these models and quantitatively evaluate their performance on Tuberculosis Chest X-rays, Ovarian Tumors, and Liver Segmentation datasets. Finally, we discuss the main challenges and future trends in medical image segmentation. Our work can assist researchers in the related field to quickly establish medical segmentation models tailored to specific regions.

Authors

  • Wenjian Yao
    Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, 610054, Chengdu, China.
  • Jiajun Bai
    Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, 610054, Chengdu, China.
  • Wei Liao
    Department of Surgery, Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yuheng Chen
    Department of Computer Science, State University of New York at Binghamton, Binghamton, New York, USA.
  • Mengjuan Liu
    Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, 610054, Chengdu, China. mjliu@uestc.edu.cn.
  • Yao Xie
    Georgia Institute of Technology.

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