SEGMENTATION OF KIDNEY TUMOR BASED ON IMPROVED UNET++

Aimed at the problems that the time-consuming and subjective factors affecting the artificial segmentation of renal tumor region in CT images, an automatic segmentation method for renal tumor based on convolution neural network is proposed. Unet++ segmentation network was used as the basic framework. Four feature extraction modules in the pre-trained ResNet-34 network were used as Unet++ network feature encoders to extract image feature information. The redesigned atrous space pyramid pooling network was embedded in each decoding path of the Unet++. The renal tumor segmentation results were obtained by feature fusion of different decoding paths. Validation was performed on the dataset provided by the KiTS19 competition. The experimental results show that the algorithm effectively improves the segmentation accuracy of kidney tumor CT images.