Accurate Cell Segmentation Based on Generative Adversarial Networks and Nuclei Guide Factors

The accurate segmentation of cells in cervical images is crucial for the recognition of pathological situations and the estimation of their severity. In this work, we investigate the segmentation of both the nucleus and the cytoplasm of each cell based on two generative adversarial networks (GANs). First, we detect the location of the nucleus with the extraction of the nucleus boundaries in each cell, which is obtained by the training of the Nucleus-GAN. The segmented nucleus area serves as a guide factor for the definition of the cell boundary, and it is used as input in the Cell-GAN, for the segmentation of the cell boundaries. As it is verified by the experimental results, the proposed method is efficient and leads to accurate nucleus and cell boundaries, presenting high performance.