痔病理图像自动化分割视觉大模型的构建及其在中医临床证型分析中的应用OA

This paper proposes a two-stage method integrating visual foundation models(VFM)and diffusion models.The segment anything model(SAM)as VFM is combined with the SegRefiner diffusion model to construct the SAM-SegRefiner framework for automated segmentation of edema,inflammation,and thrombus regions in histo-pathological images of hemorrhoidal tissue,providing a reproducible technical tool for the objective quantification of pathological morphology and its application in traditional Chinese medicine(TCM)syndrome research.Trained and validated on multi-center retrospective data,the SAM-SegRefiner model achieved an average pixel accuracy of 95.32％and a mean intersection over union(mIoU)of 66.81％on an independent test set,significantly outperfor-ming comparative models such as U-Net,MixU-Net,and SAM-Med2D,and also demonstrating robust cross-center generalization capability.Furthermore,by correlating the quantitatively segmented results from the model with the patients'TCM syndrome types,the potential associations between pathomorphological features and TCM syndrome differentiation have been explored.The analysis revealed no statistically significant differences in the degree of inflam-matory infiltration and thrombus formation among different syndrome types,suggesting a complex relationship between local pathological changes and systemic syndrome manifestations.