CMFR-Net: Cross Multi-Scale Features Refinement Network for Medical Image Segmentation

Abstract

The automation of medical image segmentation can assist doctors in quickly and accurately extracting lesion regions, reducing their workload in clinical analysis, improving diagnostic efficiency, and aiding in the early diagnosis and analysis of diseases. However, medical images are susceptible to noise, and variations in the position, size, and shape of organs and tissue structures across different patients pose significant challenges in achieving accurate segmentation. In this paper, we propose the Cross Multi-scale Features Refinement Network (CMFR-Net), which introduces the cross features enhancement (CFE) module, the boundary refinement (BR) module, and the global context features guidance (GCFG) module to extract multi-scale spatial information and boundary details of the target region, capture long-range feature dependencies, and improve segmentation performance. The CFE module captures local feature information from target regions at different scales, the BR module alleviates boundary blurring issues during segmentation, and the GCFG module strengthens the model's ability to capture global features and spatial positional information. Experiments conducted on three public datasets and one private dataset demonstrate the effectiveness of the proposed CMFR-Net. The Dice coefficients of CMFR-Net on the four datasets reached 87.35%, 87.65%, 97.52%, and 88.38%, respectively.