A semantic segmentation algorithm supported by image processing and neural network

The accurate segmentation of the lesion area is of great significance to the actual medical treatment. However, the segmentation results of the current segmentation network are not accurate enough to provide guidance for actual medical treatment. To solve this problem, a improved U-Net segmentation network is proposed. Firstly. The residual module and new attention mechanism are introduced to optimize the encoder, and 2×2 convolution is used instead of pooling operation, which can refine and extract features while retaining spatial feature information. Secondly, the attention mechanism is introduced before the upsampling jump connection, so that the network pays attention to the spatial information of the low-level feature map. The improved U-Net segmentation network was evaluated on the LiTS datasets. Compared with the traditional If-Net, the Dice coefficient and recall rate are increased by 5.6% and 3.03 % respectively in the liver segmentation task, the Dice coefficient and recall rate are increased by 7.51% and 8.8% respectively in the liver tumor segmentation task.