  Background and objective: In this paper, a modified U-Net based framework is
presented, which leverages techniques from Squeeze-and-Excitation (SE) block,
Atrous Spatial Pyramid Pooling (ASPP) and residual learning for accurate and
robust liver CT segmentation, and the effectiveness of the proposed method was
tested 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 each
convolution in the U-Net encoder, while suppressing irrelevant regions, and
highlighting features of specific segmentation task; Secondly, ASPP was
employed to replace the transition layer and the output layer, and acquire
multi-scale image information via different receptive fields. Thirdly, to
alleviate the degradation problem, the traditional convolution block was
replaced with the residual block and thus prompt the network to gain accuracy
from considerably increased depth.
  Results: In the LiTS17 experiment, the mean values of Dice, VOE, RVD, ASD and
MSD were 95.71, 9.52, -0.84, 1.54 and 29.14, respectively. Compared with other
closely related 2D-based models, the proposed method achieved the highest
accuracy. 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. Compared
with other closely related models, the proposed method achieved the highest
segmentation accuracy except for the RVD.
  Conclusion: The proposed model enables a great improvement on the accuracy
compared to 2D-based models, and its robustness in circumvent challenging
problems, such as small liver regions, discontinuous liver regions, and fuzzy
liver boundaries, is also well demonstrated and validated.
