Improved U-net kidney tumor segmentation method

The invention discloses an improved U-net kidney tumor segmentation method. The method comprises the following steps: combining a kidney and a kidney tumor together through a pixel superposition method; extracting the image for a plurality of times by using convolution at the encoder part, and generating corresponding segmented kidney and kidney tumor images by using transposed convolution at thedecoder part; introducing a residual learning unit into the encoder; and carrying out optimization training by using a Luoshi loss function in network training. According to the method, a deep residual network and a The Lovasz-Softmax loss function are combined to achieve the semantic segmentation method based on the ResUnet, the characteristics of the image are better extracted by utilizing the Lovasz hinge residual network, the improved method is more excellent than an original method, and the segmentation precision of the kidney tumor is effectively improved. 本发明公开了一种改进U-net的肾脏肿瘤分割方法，步骤为：通过像素叠加的方法将肾脏和肾脏肿瘤合并在一起；在编码器