From CNN to Transformer: A Review of Medical Image Segmentation Models
Wenjian Yao Jiajun Bai Wei Liao Yuheng Chen Mengjuan Liu Yao Xie
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. Additionally, 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.
In this paper, we conduct a survey of the most representative four medical
image segmentation models in recent years. We theoretically analyze the
characteristics of these models and quantitatively evaluate their performance
on two benchmark datasets (i.e., Tuberculosis Chest X-rays and ovarian tumors).
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.