CotepRes-Net: An efficient U-Net based deep learning method of liver segmentation from Computed Tomography images

Automatic liver segmentation from CT images is challenging due to the indistinct boundaries between the liver and surrounding organs in the abdominal cavity CT. To address these limitations, we propose a deep learning model called CotepRes-Net, which is based on the U-Net architecture but with modified skip connection using a convolutional block attention module (CBAM). We introduce a modified inception block and a contextual transformer block (CoT) at the bottleneck to capture richer feature representations and leverage global contextual information to constrain the liver shape at the unclear boundary. To accelerate the network convergence in the training stage, we utilize an Efficient channel attention module (ECA) in the decoder layers and replace the convolution operation in each convolution layer with a residual structure. We evaluate the performance of the proposed model using the publicly available dataset LiTS (MICCAI 2017 Liver Tumor Segmentation Challenge) and show the segmentation results on the dataset Sliver07 (Segmentation of the Liver Competition 2007). The results show that our model achieves a dice similarity coefficient (DSC) of 0.9688, Jaccard index of 0.9422, volumetric overlap error (VOE) of 0.0578, relative absolute volume difference (RAVD) of 0.0039, average symmetric surface distance (ASSD) of 1.094 mm and maximum symmetric surface distance (MSSD) of 16.079 mm for liver segmentation. The experimental results show that the proposed method is comparable to or even outperform the state-of-the-art methods in terms of accuracy and computation cost, which can serve as a valuable tool for image-based liver analysis.