Construction of An Automated Segmentation Visual Foundation Model for Pathological Images of Hemorrhoids and Its Application in Traditional Chinese Medicine Clinical Syndrome Analysis

ZHANG Shijie; ZHANG Ao; WANG Kang; KANG Bin; YU Xiaofan; FENG Xujing; CAO Jinyu; HUANG Wenzhen; DING Kang

doi:10.13288/j.11-2166/r.2026.07.011

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Construction of An Automated Segmentation Visual Foundation Model for Pathological Images of Hemorrhoids and Its Application in Traditional Chinese Medicine Clinical Syndrome Analysis

更新时间：2026-04-01

- Construction of An Automated Segmentation Visual Foundation Model for Pathological Images of Hemorrhoids and Its Application in Traditional Chinese Medicine Clinical Syndrome Analysis
  增强出版
- Journal of Traditional Chinese Medicine Vol. 67, Issue 7, Pages: 764-769(2026)
- 作者机构：
  
  1.南京中医药大学附属南京市中医院，江苏省南京市秦淮区大明路157号，210022
  2.南京邮电大学物联网学院
- 作者简介：
- 基金信息：
- DOI：10.13288/j.11-2166/r.2026.07.011
  CLC：
- Received：30 October 2025，
  
  Revised：2026-01-20，
  
  Published：02 April 2026
- 稿件说明：
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张诗杰,张澳,王康等.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[J].中医杂志,2026,67(07):764-769.

ZHANG Shijie,ZHANG Ao,WANG Kang,et al.Construction of An Automated Segmentation Visual Foundation Model for Pathological Images of Hemorrhoids and Its Application in Traditional Chinese Medicine Clinical Syndrome Analysis[J].Journal of Traditional Chinese Medicine,2026,67(07):764-769.
张诗杰,张澳,王康等.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[J].中医杂志,2026,67(07):764-769. DOI： 10.13288/j.11-2166/r.2026.07.011.

ZHANG Shijie,ZHANG Ao,WANG Kang,et al.Construction of An Automated Segmentation Visual Foundation Model for Pathological Images of Hemorrhoids and Its Application in Traditional Chinese Medicine Clinical Syndrome Analysis[J].Journal of Traditional Chinese Medicine,2026,67(07):764-769. DOI： 10.13288/j.11-2166/r.2026.07.011.

摘要

提出一种融合视觉大模型与扩散模型的两阶段方法，采用分割一切模型（SAM）作为视觉大模型代表，结合SegRefiner扩散模型构建SAM-SegRefiner模型框架，用于痔核组织病理图像中水肿、炎症及血栓区域的自动化分割，为病理形态的客观量化及其中医证候学研究提供可重复的技术工具。通过多中心回顾性数据进行训练与验证，SAM-SegRefiner模型在独立测试集上平均像素准确率达95.32%，平均交并比为66.81%，显著优于U-Net、MixU-Net、SAM-Med2D等对比模型，并展现出良好的跨中心泛化能力。进一步将模型量化分割结果与患者中医证型进行关联分析，探索病理形态学特征与中医辨证分型之间的潜在联系，发现不同证型间的炎症浸润与血栓形成程度未呈现统计学显著差异，提示局部病理改变与整体证候表征之间关系复杂。

Abstract

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 histopathological 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 inflammatory infiltration and thrombus formation among different syndrome types， suggesting a complex relationship between local pathological changes and systemic syndrome manifestations.

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references

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