Semantic segmentation method of 3D liver image based on contextual attention model

Sai Shao, Xiaolong Zhang, Ruoqin Chen, Chunhua Deng

Abstract: Aiming at the problems of difficult segmentation, time-consuming and low precision of 3D liver medical images, 3D liver image semantic segmentation method based on context attention strategy is proposed. This method combines the three-dimensional spatial boundary information on the upper liver features map and the overall channel information on the lower liver features map. The model used the LiTS data set for ablation experiments in comparison with the previous model. Experimental results show that the Dice similarity coefficient (DICE) is increased by 1.6%, and the volume overlap error (VOE) is reduced by 2.49% in comparison with Channel-Unet. The relative volume difference (RVD), the average symmetric surface distance (ASD) and the root mean square symmetric surface distance (RMSD) which indicates that the algorithm improves the performance of 3D liver medical images segmentation. Extended experiments were carried out on the Sliver07 data set, the CT data set in CHAOS and the clinical MRI liver medical imaging data set of a hospital, and the results indicated that the proposed method has strong generalization ability.