Research on kidney segmentation algorithm based on mask estimation

The current mainstream methods are to use 2D segmentation algorithms such as UNet and FCN to segment kidneys and tumors on a single slice of a patient, but these methods will lose more contextual information of the original image, which is not conducive to the improvement of segmentation accuracy. To solve this problem, this paper proposes an improved Res-UNet image segmentation algorithm (M-ResUNet) based on mask estimation and Res-UNet image segmentation algorithm. The algorithm first uses the mask estimation algorithm to obtain the context information and the region of interest of the image to be segmented, then uses the Res-UNet image segmentation algorithm to segment the kidney, and finally uses the image fusion algorithm to fuse the mask image and the segmented image. The algorithm makes the segmented images retain the accurate segmentation results in the segmented images to the greatest extent, and removes the inaccurate segmentation results in the segmented images. The experimental results show that the MResUNet kidney segmentation algorithm proposed in this paper has a certain improvement in the segmentation accuracy of the KITS19 kidney public dataset.