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

作　　者：Nischita Kaza Ashkan Ojaghi Francisco E.Robles 

机构地区：[1]School of Electrical and Computer Engineering,Georgia Institute of Technology,Atlanta,Georgia,USA [2]Wallace H.Coulter Department of Biomedical Engineering,Georgia Institute of Technology and Emory University,Atlanta,Georgia,USA

出　　处：《Biomedical Engineering Frontiers》2022年第1期309-322,共14页生物医学工程前沿（英文）

基　　金：support for this work by the Massner Lane Family Foundation;Burroughs Wellcome Fund (CASI BWF 1014540);National Science Foundation (NSF CBET CAREER 1752011);the Donaldson Charitable Trust Research Synergy Fund Award。

摘　　要：Objective and Impact Statement.We 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 analyzers.Introduction.Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel,costly chemical reagents,and lengthy protocols.Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow.In 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 smears.Methods.We train independent deep networks to virtually stain and segment grayscale images of smears.The segmented images are then used to train a classifier to yield a quantitative five-part WBC differential.Results.Our virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining,the gold standard in hematology.The trained cellular and nuclear segmentation networks achieve high accuracy,and the classifier can achieve a quantitative five-part differential on unseen test data.Conclusion.This 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 

分 类 号：TH776[机械工程—仪器科学与技术] R318[机械工程—精密仪器及机械]