An overview of abdominal segmentation methods using General Adversarial Network (GAN)

Applications ranging from image-guided surgery to computer-assisted diagnosis depend on the segmentation of abdominal anatomy. The uses of the General Adversarial Network (GAN) in the automatic segmentation of abdominal pictures are discussed in this review article. Discussion in this paper starts off by reviewing the underlying theory of GAN and its variations, then covering the most recent abdominal segmentation techniques. In addition, it introduces the variety of applications and datasets included for these imaging studies. The quantitative performances (evaluated using Dice Similarity Coefficient and F-scores) of 13 representative studies are summarized and compared. Overall, a more precise abdominal segmentation can be achieved using GAN-based methods of image analysis.