Abstract: Liver disease is one of the most common diseases in medicine,and accurate segmentation of liver disease is a necessary step to assist liver disease diagnosis and surgical planning.However,liver segmentation is still a challenging task due to the complexity of liver CT images.With the deepening of research,people begin to consider combining high-level semantics with low-level semantics to further enhance the segmentation effect.However,most of previous studies simply use splicing or summation operation to fuse different semantics,resulting in failure to make full use of its complementarity.To solve the above problems,a network(MD-AUNet) based on multi-scale feature fusion and dual attention mechanism is proposed in this paper.Firstly,the hierarchical dual attention mechanism in the hierarchical multi-scale attention down-sampling module(HAM) is used to effectively fuse feature information of different scales and extract feature representations rich in spatial information.Then,the global context of high-level features is obtained through the global attention up-sampling module(GAM) for weighting the low-level feature information,so as to select more accurate spatial information.At the same time,deep supervision strategy is used in network training to learn the hierarchical representation of different decoding layers.Moreover,a concise and effective post-processing method is proposed to refine the coarse segmentation result of MD-AUNet.Experimental results on the liver datasets collected by the hospital(manually annotated by experts) demonstrate that the proposed algorithm is superior to other existing liver segmentation algorithms in subjective visual perception and objective evaluation indicators,and its mean pixel accuracy,mean IoU and Dice are 97.6%,95.4%,and 95.5% respectively.

Key words: CT image, Liver segmentation, Multi-scale feature fusion, Dual attention mechanism, MD-AUNet