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Background and objective: In this paper, a modified U-Net based framework ispresented, which leverages techniques from Squeeze-and-Excitation (SE) block,Atrous Spatial Pyramid Pooling (ASPP) and residual learning for accurate androbust liver CT segmentation, and the effectiveness of the proposed method wastested on two public datasets LiTS17 and SLiver07.  Methods: A new network architecture called SAR-U-Net was designed. Firstly,the SE block is introduced to adaptively extract image features after eachconvolution in the U-Net encoder, while suppressing irrelevant regions, andhighlighting features of specific segmentation task; Secondly, ASPP wasemployed to replace the transition layer and the output layer, and acquiremulti-scale image information via different receptive fields. Thirdly, toalleviate the degradation problem, the traditional convolution block wasreplaced with the residual block and thus prompt the network to gain accuracyfrom considerably increased depth.  Results: In the LiTS17 experiment, the mean values of Dice, VOE, RVD, ASD andMSD were 95.71, 9.52, -0.84, 1.54 and 29.14, respectively. Compared with otherclosely related 2D-based models, the proposed method achieved the highestaccuracy. In the experiment of the SLiver07, the mean values of Dice, VOE, RVD,ASD and MSD were 97.31, 5.37, -1.08, 1.85 and 27.45, respectively. Comparedwith other closely related models, the proposed method achieved the highestsegmentation accuracy except for the RVD.  Conclusion: The proposed model enables a great improvement on the accuracycompared to 2D-based models, and its robustness in circumvent challengingproblems, such as small liver regions, discontinuous liver regions, and fuzzyliver boundaries, is also well demonstrated and validated.

SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid  pooling based residual U-Net for automatic liver segmentation in Computed  Tomography