Image segmentation method based on residual U-network and double discriminant networks
Generative Adversarial Networks are widely used in the field of images,but relatively little research has been done on their application in the field of cell nucleus image segmentation.Since the accurate segmentation of cell nucleus is extremely helpful for pathological diagnosis work,an image segmentation method based on residual U-network and Generative Adversarial Network is proposed.The network uses ResUNet network as a generative network and Image GAN as double discriminant network,and the training process is optimized using a double loss function and freezing strategy.The evaluation metrics MioU,Dice and Acc of the improved network on the PanNuke dataset are 80%,93%and 80%,which are improved by 2.3%,1.7%and 2.1%,respectively,compared to the experimental results of the ResUNet network.The experimental results proved that the improved network has a good accuracy rate for cell nucleus segmentation,which can be used as an important basis for pathological diagnostic.
万方数据
维普
