A stroke lesion segmentation method based on multi-scale and cascade attention mechanisms

Stroke, as a disease with high incidence and mortality rates, is increasingly receiving attention. Despite the rapid development of deep learning in the medical field providing excellent performance for AI-assisted diagnosis, automated segmentation of stroke still poses significant challenges. Issues such as the similarity between hemorrhagic regions and the background, the irregularity of hemorrhagic areas, and the vast variability in hemorrhage sizes persist. To address these segmentation challenges, this paper introduces a new network architecture that incorporates a multi-scale channel joint attention module and cascaded feature assisted enhancement module, taking into account the anisotropy and asymmetry of images. This method aims to accurately segment lesion tissues from chronic stroke brain images in T1- weighted MRI. Experimental results have demonstrated that the method proposed in this paper achieves superior performance outcomes compared to other methods. This study provides a promising solution for stroke segmentation.