刑事技术2026，Vol.51Issue(2)：121-128,8.DOI:10.16467/j.1008-3650.2025.0003
基于CLIP模型的苏丹Ⅲ染色切片扫描图像脂滴分割研究
Segmentation of Fat Droplets in Whole Slide Images of Tissue Stained with Sudan Ⅲ
摘要
Abstract
In forensic pathology,Sudan Ⅲ staining is used to confirm fat embolism,and its quantitative grading is of significant importance in determining the cause of death.However,manual grading based on microscopic observation is highly dependent on personal experience.To objectively quantify the degree of fat embolism,we explored a method for automatic segmentation of fat droplets in Whole Slide Images(WSI)of lung tissue stained with Sudan Ⅲ.Although the colors of the Sudan Ⅲ stained sections are simply consisted of transparent tissue and scarlet fat droplets,issues such as residual dye,uneven staining of the fat droplets,irregular shapes,and significant size differences can lead to missegmentation and insufficient segmentation accuracy.To address this,we propose a contrastive language-image pre-training(CLIP)model framework combined with prompt learning for fat droplet segmentation:first,feature maps output by the CLIP image encoder are fused through skip connections,guiding the model to accurately segment fat droplets using CLIP's prior knowledge via text prompts;then,a dice loss function is used to alleviate the imbalance between the foreground and background of the image;finally,validation is performed on the slice dataset and compared with U-Net,FCN8s,and Unet++models.The results indicate the method proposed in this article is superior to others in segmenting fat droplets on stained slice images.Moreover,the proposed cross-modal prompt learning can be integrated into other large segmentation models to perform specific target segmentation tasks.关键词
法医病理学/脂肪栓塞/特殊染色/图像分割/对比语言-图像预训练(CLIP)/深度学习/全视野数字图像Key words
forensic pathology/fat embolism/special staining/image segmentation/contrastive language-image pre-training(CLIP)/deep learning/digital whole slide images分类
社会科学引用本文复制引用
王子夜,汤晓蕙,周兰,许春燕,周顺平,张开乔,刘方舟,周盛斌..基于CLIP模型的苏丹Ⅲ染色切片扫描图像脂滴分割研究[J].刑事技术,2026,51(2):121-128,8.基金项目
公安部应用创新计划(2022YY15) （2022YY15）
江苏省公安厅厅级科研项目(2021KO004) （2021KO004）
江苏省基础研究计划自然科学基金面上项目(BK20241991) （BK20241991）
