Liver Image Segmentation Algorithm Based on Watershed Correction and U-Net
摘要
When Convolutional Neural Network(CNN) is used for liver image segmentation with blurred boundaries,the segmentation precision is reduced due to the frequent loss of location information.To address the problem,this paper proposes an automated liver image segmentation algorithm that combines the watershed correction and the U-Net model.The algorithm takes advantages of U-Net in layered learning of image features,so as to achieve fusion of shallow features and deep features without loss of detailed information,such as the location of the target.After the initial result of liver image segmentation is obtained,the boundaries of the initial result is corrected by using blocks formed by the watershed algorithm,so as to obtain a segmentation result with smooth and precise boundaries.Experimental results show that the proposed algorithm can implement more precise liver image segmentation compared with the existing graph-cut algorithm and the Fully Convolutional Network(FCN) algorithm.