首页|基于全偏振显微成像的数字病理技术

基于全偏振显微成像的数字病理技术

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
数字病理技术利用经过数字化的病理样本显微图像及其特征，并配合人工智能技术，实现生物组织病变特征的定量评估和判定，辅助临床医生做出诊断结论。利用偏振光照明和偏振探测可以实现全偏振成像，图像每个像素的偏振特征都包含更加丰富的信息，特别是普通光学成像难以获得的亚细胞超分辨微观结构特征信息，可为病变组织的识别和定量评估提供更为有效的手段。本文总结了全偏振成像技术，并结合典型临床应用归纳总结了全偏振显微图像的数据分析方法和最新进展。
Digital Pathology Based on Fully Polarized Microscopic Imaging
Significance Digital pathology uses digitized pathological images and their features in conjunction with artificial intelligence technology to achieve quantitative characterization of cancerous tissues and assist pathologists in clinical diagnoses.The use of polarized light illumination and polarized light detection can achieve full polarization imaging.Accordingly,the polarization characteristics of each pixel of the image contain abundant microstructural information,especially subcellular super-resolution information,that is difficult to obtain with nonpolarization imaging.Polarization imaging can provide a more effective means for the identification and quantitative characterization of cancerous tissues.This paper introduces Mueller matrix microscopic imaging techniques and comprehensively reviews the latest methods for polarization feature extraction,including supervised learning-based polarization pixel and image feature extraction,unsupervised learning-based polarization pixel clustering,and the extension of annotations through polarization feature templates based on super-pixels,highlighting their potential clinical applications.Progress Mueller matrix imaging provides abundant subcellular-level information on tissue microstructures.The quantitative extraction of polarization features from Mueller pixels is crucial for the clinical application of polarization imaging.In contrast to stain image-based digital pathology,polarization feature extraction through supervised learning offers more abundant microstructural information.However,the reliance on extensive,well-annotated data poses time and labor challenges.Moreover,supervised learning is dependent on pathologists'prior knowledge,limiting the comprehensive utilization of information from the polarization space.Unsupervised clustering methods facilitate the decomposition of pathological tissues into distinct microstructural subtypes,enhancing the exploration of the rich information embedded in Mueller pixels.Additionally,this approach provides evidence for the ongoing discovery of new physical properties,structural characteristics,and dynamic processes at all levels above the subcellular scale in organisms,including living entities.Conclusions and Prospects Following advancements in molecular biology techniques,the specific identification of molecular components in biological entities is becoming a pivotal tool in biomedical research,thus leading to diverse omics approaches.Polarization-based digital pathology can leverage feature extraction methods developed in various omics approaches.The unsupervised clustering of Mueller pixels quantitatively extracts information at various levels above the subwavelength scale,enabling the integration of label-free,noninvasive,abundant information features of Mueller matrix imaging into novel spatiotemporal omics methods.

medical opticsfully polarized microscopic imagingpolarization-based digital pathologypolarization feature extractionmachine learning

姚悦、裴浩杰、李浩、万嘉晨、陶丽丽、马辉

展开 >

清华大学深圳国际研究生院,广东深圳 518055

广东省偏振光学检测与成像工程技术研究中心,广东深圳 518055

北京大学深圳医院病理科,广东深圳 518036

医用光学 全偏振显微成像 偏振数字病理 偏振特征提取 机器学习

国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金深圳市基础研究学科布局项目

62375152119742066152782611374179111741781097411460778044JCYJ20170412170814624

2024

10.3788/CJL231462

中国激光
中国光学学会　中科院上海光机所

中国激光

CSTPCD北大核心
影响因子：2.204
ISSN：0258-7025
年,卷(期)：2024.51(9)
  • 57