Multi-scale Attention Module U-Net liver tumour segmentation method

Abstract

Liver tumour is a general term for tumour lesions occurring in the liver, which is the third leading cause of tumour death in the world. Most liver tumours are small in size, variable in shape and unfixed in position, and the existing neural network is not fully utilized in the segmentation of liver tumours. In order to make up for the problem of traditional tumour segmentation, improve the precision and effect of tumour treatment. The multi-scale Attention Module U-NET (MAM U-NET) was proposed by analyzing CT image data of liver tumours. The MAM U-NET model consists of a newly designed Combination module and an MAM attention module. Each MAM attention module is composed of two parts: spatial attention model and channel attention model, which enable the model to learn more extensive and rich context-dependent information and spatial information. Finally, a comparison experiment was conducted in the LITS data set to verify the performance of the model. Liver segmentation achieved the optimal result, and the average Dice coefficient reached 96.8282%. The segmentation results of liver tumours were second only to nnU-Net, and the average Dice coefficient was 60.5991%, which proved that the MAM U-NET model had better performance in the segmentation of liver tumours.