SCTransNet: 3D Medical Image Segmentation Model Based on the Fusion of CNN and Transformer

Deep learning has played an important role in medical image segmentation of liver and liver tumors, but existing models are still insufficient in accuracy and efficiency. In this paper, We proposed SCTransNet, a 3D image segmentation model based on the fusion of CNN and Transformer model. SCTransNet combines the feature extraction and expression capabilities of convolutional neural network(CNN) and the long-distance dependency modeling capability of Transformer model. SCTransNet improves the embedding layer and position encoding layer of the Transformer model to enhance the global contextual feature extraction ability. Meanwhile, a spatial attention module and a channel attention module based on improved Transformer model are designed in SCTransNet to enhance the feature extraction ability of low-dimensional pixel information and high-dimensional semantic information. Experimental results on the public dataset show that SCTransNet achieve relatively better performance than state-of-the-art methods.