A framework for semantic segmentation of pathological tissue slices

Abstract

The study of cell nuclei is the starting point of pathological analysis and new drug development in modern medicine [1], and nuclear segmentation is a primary task of nuclear research. This paper proposes an optimization method for nuclear segmentation. It regards the Conditional Generative Adversarial Network (CGAN) [2] as the fundamental segmentation structure, segments the nuclear images by using deep learning Convolutional Neural Network (CNN) [3], and then optimizes and improves the generator, discriminator, and objective function. The experimental results demonstrate that the improved UGAN has superior performance on the semantic segmentation task of the images of pathological tissue slices and can be used as a tool for automatic segmentation of pathological tissue sections.