A Three-Stage Kidney and Tumor Segmentation Method Based on the Combination of Mask RCNN and U-Net

The automatic segmentation for abdominal computed tomography（CT） images was difficulty due to low contrast ratio of tissue details and irregular shapes of kidney and tumor. A three-stage kidney and tumor segmentation method based on the combination of Mask R-CNN and U-Net was proposed. Firstly, the kidneys in the tomographic sequence images was identified by using the Mask R-CNN network, and the number of slices was recorded when the kidney appeared and disappeared in CT image, so the target range was narrowed. Secondly, the kidney and tumor were segmented, and the tomographic slices containing the tumor were summarized. The more accurate global location features and local detail features were obtained by using the network which based on U-Net, and increased dense connections in down-sampling, and used the bicubic interpolation in up-sampling. Thirdly, the segmentation of the tumor was continued, and the results was amalgamated with the previous stage. Finally, the segmentation results were further optimized by using the method based on three-dimensional connected domains. Experiments show that the proposed method has an average Dice coefficient of 0.957 20 and 0.816 36 for kidney and tumor segmentation on the KiTS19 dataset. Compared with other CNN-based methods, the segmentation accuracy is improved, which helps to achieve the automatic segmentation of kidney and tumor segmentation.