An Accurate Segmentation Method for Experimental Image of Cells

Image segmentation is not only a basic problem in the field of image processing, but also in the field of biological data processing. In recent years, people have made a lot of efforts to improve the accuracy of segmentation. This paper proposes an image segmentation method based on generative adversarial networks to segment the images of human HT29 colon cancer cells. The network uses Unet as the generator network. The segmentation results of the human HT29 colon cancer cells image dataset show that compared with other methods, this method improves the average accuracy of image segmentation to 98.659%.

I. Introduction

Disease is an important factor affecting health, and cancer is a highly malignant disease. Compared with other diseases, it has more obvious symptoms, low cure rate, and cancer patients suffer great physical and mental pain, so early cancer prevention and detection is particularly important.