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Medical image processing plays an increasingly important role in clinical diagnosis and treatment. Using the results of kidney CT image segmentation for three-dimensional reconstruction is an intuitive and accurate method for diagnosis. However, the traditional image segmentation algorithm has poor performance due to the large difference of noise between kidney and CT images, and the manual segmentation by doctors will take a very long time and is inefficient. In this paper, we propose an in-depth learning automatic segmentation method for kidney tumors, including preprocessing of training data, network model used in training process, loss function and post-processing, etc. The results show that the average dice of kidney with tumor was 0.97 in the test set.

Kidney Tumor Segmentation Based on U-Net and V-Net with Double Loss Function Training