Multi-scale context-aware segmentation network for medical images

Aiming at the problems that the method based on U-shaped network for medical image segmentation cannot capture the long-range dependencies and could lose some detail information, a multi-scale context-aware segmentation network for medical images is proposed. The model extracts the last three layer features of the encoder, and then introduces a global circular convolution transformer module to solve the problem of long-range dependencies capturing by modeling the global context information. Then, an attention guidance module is introduced to fuse features of different scales, so as to solve the problem of losing details while reducing the introduction of noise information in the low level features. The experimental performance on Synapse multi-organ segmentation datasets indicates that the model produces more precise segmentation results.