Liver segmentation based on complementary features U-Net

Automatic segmentation of the liver in abdominal CT images is critical for guiding liver cancer biopsies and treatment planning. Yet, automatic segmentation of CT liver images remains challenging due to the poor contrast between the liver and surrounding organs in abdominal CT images. In this paper, we propose a novel network for liver segmentation, and the network is essentially a U-shaped network with an encoder-decoder structure. Firstly, the complementary feature enhancement unit is designed in the network to mitigate the semantic gap between encoder and decoder. The complementary feature enhancement unit is based on subtraction, which enhances the complementary features between encoder and decoder. Secondly, this paper proposes a new cross attention model that no longer generates value by convolution, which reduces redundant information and enhances the contextual information of single sparse attention by encoding contextual information by 3 x 3 convolution. The dice score, accuracy, and precision of our network on the LiTS dataset were 95.85%, 97.19%, and 97.11%, and the dice score, accuracy, and precision on the dataset consisted of 3Dircadb and CHAOS were 93.65%, 94.38%, and 97.53%.