Recently,Mueller matrix(MM)polarimetric imaging-assisted pathology detection methods are showing great potential in clinical diagnosis.However,since our human eyes cannot observe polarized light directly,it raises a n...Recently,Mueller matrix(MM)polarimetric imaging-assisted pathology detection methods are showing great potential in clinical diagnosis.However,since our human eyes cannot observe polarized light directly,it raises a notable challenge for interpreting the measurement results by pathologists who have limited familiarity with polarization images.One feasible approach is to combine MM polarimetric imaging with virtual staining techniques to generate standardized stained images,inheriting the advantages of information-abundant MM polarimetric imaging.In this study,we develop a model using unpaired MM polarimetric images and bright-field images for generating standard hematoxylin and eosin(H&E)stained tissue images.Compared with the existing polarization virtual staining techniques primarily based on the model training with paired images,the proposed Cycle-Consistent Generative Adversarial Networks(CycleGAN)-based model simplifies data acquisition and data preprocessing to a great extent.The outcomes demonstrate the feasibility of training CycleGAN with unpaired polarization images and their corresponding bright-field images as a viable approach,which provides an intuitive manner for pathologists for future polarization-assisted digital pathology.展开更多
In pathological examinations,tissue must first be stained to meet specific diagnostic requirements,a meticulous process demanding significant time and expertise from specialists.With advancements in deep learning,this...In pathological examinations,tissue must first be stained to meet specific diagnostic requirements,a meticulous process demanding significant time and expertise from specialists.With advancements in deep learning,this staining process can now be achieved through computational methods known as virtual staining.This technique replicates the visual effects of traditional histological staining in pathological imaging,enhancing efficiency and reducing costs.Extensive research in virtual staining for pathology has already demonstrated its effectiveness in generating clinically relevant stained images across a variety of diagnostic scenarios.Unlike previous reviews that broadly cover the clinical applications of virtual staining,this paper focuses on the technical methodologies,encompassing current models,datasets,and evaluation methods.It highlights the unique challenges of virtual staining compared to traditional image translation,discusses limitations in existing work,and explores future perspectives.Adopting a macro perspective,we avoid overly intricate technical details to make the content accessible to clinical experts.Additionally,we provide a brief introduction to the purpose of virtual staining from a medical standpoint,which may inspire algorithm-focused researchers.This paper aims to promote a deeper understanding of interdisciplinary knowledge between algorithm developers and clinicians,fostering the integration of technical solutions and medical expertise in the development of virtual staining models.This collaboration seeks to create more efficient,generalized,and versatile virtual staining models for a wide range of clinical applications.展开更多
目的系统评估瑞氏–吉姆萨染色在阴道炎综合诊断中的应用潜力及诊断效能。方法选取2025年1月至3月于合肥医科大学附属医院就诊的1222例患者为研究对象,采集阴道分泌物样本,分别进行湿片显微镜检查、瑞氏–吉姆萨染色显微镜检查、革兰染...目的系统评估瑞氏–吉姆萨染色在阴道炎综合诊断中的应用潜力及诊断效能。方法选取2025年1月至3月于合肥医科大学附属医院就诊的1222例患者为研究对象,采集阴道分泌物样本,分别进行湿片显微镜检查、瑞氏–吉姆萨染色显微镜检查、革兰染色显微镜检查,并检测pH、白细胞酯酶、唾液酸苷酶等功能学指标。分析三种显微镜检查方法检测白细胞(white blood cell,WBC)评分、基底旁细胞(parabasal cell,PBC)评分、需氧菌性阴道病(aerobic vaginitis,AV)背景菌评分、乳酸杆菌等级(Lactobacillus grading,LBG)、LBG评分、乳酸杆菌丰度评分、动弯杆菌评分、加德纳菌评分、真菌、滴虫和线索细胞的差异和一致性。采用Spearman相关性检验分析WBC评分、LBG、加德纳菌评分与其对应功能学指标的相关性。计算敏感度、特异性、曲线下面积(area under the curve,AUC)等指标评估瑞氏–吉姆萨染色的诊断效能。结果瑞氏–吉姆萨染色对LBG、PBC评分、AV背景菌评分、WBC评分、加德纳菌评分、动弯杆菌评分的等级判断显著高于革兰染色(P<0.0167),对加德纳菌评分的等级判断显著高于湿片法(P<0.0167),且湿片法未检出动弯杆菌。Spearman相关性检验结果显示,瑞氏–吉姆萨染色的加德纳菌评分与唾液酸苷酶、LBG与pH的相关性最好(r=0.6922、0.7861)。McNemar检验结果显示瑞氏–吉姆萨染色对真菌的检出率显著高于湿片法,对滴虫的检出率显著高于革兰染色,对线索细胞的检出率显著高于湿片法和革兰染色(P<0.0167)。观察者间一致性分析结果显示,除线索细胞(部分观察者Kappa=0.390)外,其余指标(如PBC评分、LBG、真菌、滴虫)均表现为中高度一致性。瑞氏–吉姆萨染色诊断外阴阴道假丝酵母菌病、滴虫性阴道炎、细菌性阴道病和细胞溶解性阴道病的敏感度和特异性均在90%以上。结论瑞氏–吉姆萨染色在阴道炎诊断中兼具高检出率、高一致性、低成本、易操作的优势,有望成为阴道炎综合诊断的常规方法,尤其在医疗资源有限地区具有重要推广价值。展开更多
基金Shenzhen Key Fundamental Research Project(No.JCYJ20210324120012035).
文摘Recently,Mueller matrix(MM)polarimetric imaging-assisted pathology detection methods are showing great potential in clinical diagnosis.However,since our human eyes cannot observe polarized light directly,it raises a notable challenge for interpreting the measurement results by pathologists who have limited familiarity with polarization images.One feasible approach is to combine MM polarimetric imaging with virtual staining techniques to generate standardized stained images,inheriting the advantages of information-abundant MM polarimetric imaging.In this study,we develop a model using unpaired MM polarimetric images and bright-field images for generating standard hematoxylin and eosin(H&E)stained tissue images.Compared with the existing polarization virtual staining techniques primarily based on the model training with paired images,the proposed Cycle-Consistent Generative Adversarial Networks(CycleGAN)-based model simplifies data acquisition and data preprocessing to a great extent.The outcomes demonstrate the feasibility of training CycleGAN with unpaired polarization images and their corresponding bright-field images as a viable approach,which provides an intuitive manner for pathologists for future polarization-assisted digital pathology.
基金supported by the National Natural Science Foundation of China under Grant 62371409Fujian Provincial Natural Science Foundation of China under Grant 2023J01005.
文摘In pathological examinations,tissue must first be stained to meet specific diagnostic requirements,a meticulous process demanding significant time and expertise from specialists.With advancements in deep learning,this staining process can now be achieved through computational methods known as virtual staining.This technique replicates the visual effects of traditional histological staining in pathological imaging,enhancing efficiency and reducing costs.Extensive research in virtual staining for pathology has already demonstrated its effectiveness in generating clinically relevant stained images across a variety of diagnostic scenarios.Unlike previous reviews that broadly cover the clinical applications of virtual staining,this paper focuses on the technical methodologies,encompassing current models,datasets,and evaluation methods.It highlights the unique challenges of virtual staining compared to traditional image translation,discusses limitations in existing work,and explores future perspectives.Adopting a macro perspective,we avoid overly intricate technical details to make the content accessible to clinical experts.Additionally,we provide a brief introduction to the purpose of virtual staining from a medical standpoint,which may inspire algorithm-focused researchers.This paper aims to promote a deeper understanding of interdisciplinary knowledge between algorithm developers and clinicians,fostering the integration of technical solutions and medical expertise in the development of virtual staining models.This collaboration seeks to create more efficient,generalized,and versatile virtual staining models for a wide range of clinical applications.
文摘目的系统评估瑞氏–吉姆萨染色在阴道炎综合诊断中的应用潜力及诊断效能。方法选取2025年1月至3月于合肥医科大学附属医院就诊的1222例患者为研究对象,采集阴道分泌物样本,分别进行湿片显微镜检查、瑞氏–吉姆萨染色显微镜检查、革兰染色显微镜检查,并检测pH、白细胞酯酶、唾液酸苷酶等功能学指标。分析三种显微镜检查方法检测白细胞(white blood cell,WBC)评分、基底旁细胞(parabasal cell,PBC)评分、需氧菌性阴道病(aerobic vaginitis,AV)背景菌评分、乳酸杆菌等级(Lactobacillus grading,LBG)、LBG评分、乳酸杆菌丰度评分、动弯杆菌评分、加德纳菌评分、真菌、滴虫和线索细胞的差异和一致性。采用Spearman相关性检验分析WBC评分、LBG、加德纳菌评分与其对应功能学指标的相关性。计算敏感度、特异性、曲线下面积(area under the curve,AUC)等指标评估瑞氏–吉姆萨染色的诊断效能。结果瑞氏–吉姆萨染色对LBG、PBC评分、AV背景菌评分、WBC评分、加德纳菌评分、动弯杆菌评分的等级判断显著高于革兰染色(P<0.0167),对加德纳菌评分的等级判断显著高于湿片法(P<0.0167),且湿片法未检出动弯杆菌。Spearman相关性检验结果显示,瑞氏–吉姆萨染色的加德纳菌评分与唾液酸苷酶、LBG与pH的相关性最好(r=0.6922、0.7861)。McNemar检验结果显示瑞氏–吉姆萨染色对真菌的检出率显著高于湿片法,对滴虫的检出率显著高于革兰染色,对线索细胞的检出率显著高于湿片法和革兰染色(P<0.0167)。观察者间一致性分析结果显示,除线索细胞(部分观察者Kappa=0.390)外,其余指标(如PBC评分、LBG、真菌、滴虫)均表现为中高度一致性。瑞氏–吉姆萨染色诊断外阴阴道假丝酵母菌病、滴虫性阴道炎、细菌性阴道病和细胞溶解性阴道病的敏感度和特异性均在90%以上。结论瑞氏–吉姆萨染色在阴道炎诊断中兼具高检出率、高一致性、低成本、易操作的优势,有望成为阴道炎综合诊断的常规方法,尤其在医疗资源有限地区具有重要推广价值。
