Abstract
UNet model performs well in medical image segmentation. In this paper, UNet model is improved by the same padding after each convolution, so that the image scale remains unchanged through convolution, and the edges of the image are no longer cut off. The improved UNet model is trained for semantic segmentation of the liver in the portal vein in CT images, using binary cross entropy as the loss function, and dice value as the performance evaluation index. The average dice value of the test set reaches 0.85. Our work can be used to help for daily work of liver image segmentation.
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