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您的检索式：作者名="Francisco E.Robles"
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| 1 | Virtual Staining,Segmentation,and Classification of Blood Smears for Label-Free Hematology Analysis显示文摘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. | Nischita Kaza Ashkan Ojaghi Francisco E.Robles | 2022 | Biomedical Engineering Frontiers2022，3，1： | 0 |
| 2 | Deep UV Microscopy Identifies Prostatic Basal Cells:An Important Biomarker for Prostate Cancer Diagnostics显示文摘Objective and Impact Statement.Identifying benign mimics of prostatic adenocarcinoma remains a significant diagnostic challenge.In this work,we developed an approach based on label-free,high-resolution molecular imaging with multispectral deep ultraviolet(UV)microscopy which identifies important prostate tissue components,including basal cells.This work has significant implications towards improving the pathologic assessment and diagnosis of prostate cancer.Introduction.One of the most important indicators of prostate cancer is the absence of basal cells in glands and ducts.However,identifying basal cells using hematoxylin and eosin(H&E)stains,which is the standard of care,can be difficult in a subset of cases.In such situations,pathologists often resort to immunohistochemical(IHC)stains for a definitive diagnosis.However,IHC is expensive and time-consuming and requires more tissue sections which may not be available.In addition,IHC is subject to false-negative or false-positive stains which can potentially lead to an incorrect diagnosis.Methods.We leverage the rich molecular information of label-free multispectral deep UV microscopy to uniquely identify basal cells,luminal cells,and inflammatory cells.The method applies an unsupervised geometrical representation of principal component analysis to separate the various components of prostate tissue leading to multiple image representations of the molecular information.Results.Our results show that this method accurately and efficiently identifies benign and malignant glands with high fidelity,free of any staining procedures,based on the presence or absence of basal cells.We further use the molecular information to directly generate a high-resolution virtual IHC stain that clearly identifies basal cells,even in cases where IHC stains fail.Conclusion.Our simple,low-cost,and label-free deep UV method has the potential to improve and facilitate prostate cancer diagnosis by enabling robust identification of basal cells and other important prostate tissue components. | Soheil Soltani Brian Cheng Adeboye O.Osunkoya Francisco E.Robles | 2022 | Biomedical Engineering Frontiers2022，3，1： | 0 |
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