GS_NeXt: Graph theory combining segment anything model for liver and tumor segmentation from CT

The static convolutional network is designed with a restricted sense field, but it limits the global feature extraction. While dynamic convolution addresses the issue of limited static receptive fields, it struggles to perform well in discontinuous liver regions, liver tumor border regions, and microtumor segmentation. To alleviate the above issues, we proposed a network, GS_NeXt, based on graph theory and the Segment Anything Model (SAM) for liver and tumor segmentation from CT scans. First, we employed the feature extraction module of ConvNeXt-v2 to learn features across channels, enabling the network to focus on critical liver and tumor regions. Second, we utilized the SAM with frozen weights to extract more comprehensive global information, thereby enhancing feature representation. Third, we applied graph reasoning to globally model unstructured local features, improving the network's understanding of CT images in discontinuous liver regions, liver-tumor boundaries, and microtumor areas. Finally, we incorporated a deep supervision mechanism to facilitate the learning of multi-scale features throughout the network. We evaluated the proposed segmentation method for two publicly available abdominal liver tumor CT datasets. On the LiTS17 dataset, GS_NeXt achieved 97.74% and 87.25% on the Dice scores, 1.01 and 2.23 mm on the average symmetric surface distance (ASD), and 3.68% and 22.60% on the volume overlap errors (VOE) for liver and tumor segmentation, respectively. On the 3DIRCADb dataset, it achieved 97.31% and 87.36% on the Dice scores, ASD values of 1.01 and 2.12 mm, and VOE scores of 3.56% and 21.56% for liver and tumor segmentation, respectively.