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Virtual Staining,Segmentation,and Classification of Blood Smears for Label-Free Hematology Analysis

Biomedical Engineering Frontiers 2022年第1期3卷 309-322页

作者： nischita kaza Ashkan Ojaghi Francisco E.Robles School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGeorgiaUSA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory UniversityAtlantaGeorgiaUSA

Objective and Impact *** present a fully automated hematological analysis framework based on single-channel(single-wavelength),label-free deep-ultraviolet(UV)microscopy that serves as a fast,cost-effective alternative to conventional hematology *** analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel,costly chemical reagents,and lengthy ***-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler *** this work,we leverage the unique capabilities of deep-UV microscopy as a label-free,molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining,segmentation,classification,and counting of white blood cells(WBCs)in single-channel images of peripheral blood *** train independent deep networks to virtually stain and segment grayscale images of *** segmented images are then used to train a classifier to yield a quantitative five-part WBC *** virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining,the gold standard in *** trained cellular and nuclear segmentation networks achieve high accuracy,and the classifier can achieve a quantitative five-part differential on unseen test *** proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple,fast,and low-cost,point-of-care hematology analyzer.

关键词： networks classifier Blood

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