Automated detection of GFAP-labeled astrocytes in micrographs using YOLOv5

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dc.authorid0000-0002-9282-6568
dc.contributor.authorHuang, Yewen
dc.contributor.authorKruyer, Anna
dc.contributor.authorSyed, Sarah
dc.contributor.authorKayasandık, Cihan Bilge
dc.contributor.authorPapadakis, Manos
dc.contributor.authorLabate, Demetrio
dc.date.accessioned2023-01-09T07:22:29Z
dc.date.available2023-01-09T07:22:29Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAstrocytes, a subtype of glial cells with a complex morphological structure, are active players in many aspects of the physiology of the central nervous system (CNS). However, due to their highly involved interaction with other cells in the CNS, made possible by their morphological complexity, the precise mechanisms regulating astrocyte function within the CNS are still poorly understood. This knowledge gap is also due to the current limitations of existing quantitative image analysis tools that are unable to detect and analyze images of astrocyte with sufficient accuracy and efficiency. To address this need, we introduce a new deep learning framework for the automated detection of GFAP-immunolabeled astrocytes in brightfield or fluorescent micrographs. A major novelty of our approach is the applications of YOLOv5, a sophisticated deep learning platform designed for object detection, that we customized to derive optimized classification models for the task of astrocyte detection. Extensive numerical experiments using multiple image datasets show that our method performs very competitively against both conventional and state-of-the-art methods, including the case of images where astrocytes are very dense. In the spirit of reproducible research, our numerical code and annotated data are released open source and freely available to the scientific community.
dc.description.sponsorshipNational Science Foundation ; National Institutes of Healthen_US
dc.description.sponsorshipNational Science Foundation (NSF)en_US
dc.identifier.citationHuang, Y., Kruyer, A., Syed, S., Kayasandık, C. B., Papadakis, M. ve Labate, D. (2022). Automated detection of GFAP-labeled astrocytes in micrographs using YOLOv5. Scientific Reports, 12(1). https://dx.doi.org/10.1038/s41598-022-26698-7
dc.identifier.doi10.1038/s41598-022-26698-7
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.pmid36564441
dc.identifier.scopus2-s2.0-85144635943
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://dx.doi.org/10.1038/s41598-022-26698-7
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10261
dc.identifier.volume12
dc.identifier.wos000965605400064en_US
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorKayasandık, Cihan Bilge
dc.language.isoen
dc.publisherNature Research
dc.relation.ispartofScientific Reportsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subjectYOLOv5
dc.subjectGFAP-Labeled Astrocytes
dc.subjectMicrographs
dc.subjectAutomated Detection
dc.titleAutomated detection of GFAP-labeled astrocytes in micrographs using YOLOv5
dc.typeArticle

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