Segmentation of liver CT images based on the improved UNet network

In clinical medicine, the liver segmentation is indispensable for the diagnosis of liver diseases. The shape and size of the liver varies in CT images and the similar grayscale values with neighboring organ tissues, which cause difficulties for segmentation. For these problems, we propose a network for the segmentation of liver CT images, which based on encoder-decoder structure. The network applies the SE-Res block instead of the original convolution block to optimize the boundary information and apply the spatial-channel attention gate to enhance the features of the liver in the decoder. The proposed algorithm was validated on the LITS-28 dataset, the Mean Intersection over Union (MIOU) and Dice similarity coefficient (Dice) were 93.68% and 96.45%, respectively. Compared with other similar algorithms, the performance of the proposed algorithm is better and the segmented liver results are more accurate.