Algorithm for Segmentation of Liver Tumor Based on Improved U-Net Model

Aiming at the problem that the traditional U-Net network has poor segmentation effect on tumors with small volume and fuzzy tissue edges. The residual structure is introduced into the up-sampling and down-sampling modules of the original U-Net network to extract more feature information and prevent network degradation; The attention mechanism is introduced before each jump connection to focus attention on the region of interest and suppress redundant features; Group Normalization (GN) is used to replace the commonly used Batch Normalization (BN) to reduce the impact of excessively small batch size on network accuracy; focal Tversky Loss function is used to improve the data imbalance in liver tumor segmentation. Finally, the improved model is trained and tested on LiTS2017 data sets. The experimental results show that compared with the traditional U-Net, the Dice index of liver and tumor segmentation of the improved model proposed in this paper has increased by 5.14% and 2.63% respectively, and the recall rate has increased by 1.8% and 9.05%.