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EAR-U-Net: EfficientNet and attention-based residual U-Net for automatic liver segmentation in CT
In the Sliver07 evaluation, the proposed method achieved the best segmentation performance on all five standard metrics.
SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography
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.
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