A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 1; peer review: 1 not approved]

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dc.authorid0000-0003-1106-3288
dc.contributor.authorÇapar, Abdulkerim
dc.contributor.authorÇimen Yetiş, Sibel
dc.contributor.authorAladağ, Zeynep
dc.contributor.authorEkinci, Dursun Ali
dc.contributor.authorAyten, Umut Engin
dc.contributor.authorKerman, Bilal Ersen
dc.contributor.authorTöreyin, Behçet Uğur
dc.date.accessioned2021-11-01T07:25:43Z
dc.date.available2021-11-01T07:25:43Z
dc.date.issued2021
dc.departmentİstanbul Medipol Üniversitesi, Rektörlük, Rejeneratif ve Restoratif Tıp Araştırmaları Merkezi (REMER)
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü, Histoloji ve Embriyoloji Ana Bilim Dalı
dc.description.abstractMyelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by colocalization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machinelearning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitates expert labor. To facilitate myelin annotation, we developed a workflow and a software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, we shared a set of myelin ground truths annotated using this workflow.
dc.identifier.citationÇapar, A., Çimen Yetiş, S., Aladağ, Z., Ekinci, D. A., Ayten, U. E., Kerman, B. E. ... Töreyin, B. U. (2021). A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 1; peer review: 1 not approved]. F1000Research, 9, 1-10. https://dx.doi.org/10.12688/F1000RESEARCH.27139.1
dc.identifier.doi10.12688/F1000RESEARCH.27139.1
dc.identifier.endpage10
dc.identifier.issn2046-1402
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttps://dx.doi.org/10.12688/F1000RESEARCH.27139.1
dc.identifier.urihttps://hdl.handle.net/20.500.12511/8539
dc.identifier.volume9
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherF1000 Research Ltd
dc.relation.ispartofF1000Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsAttribution 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectFluorescence Images
dc.subjectImage Analysis
dc.subjectMachine Learning
dc.subjectMyelin Annotation Tool
dc.subjectMyelin Quantification
dc.titleA multi-spectral myelin annotation tool for machine learning based myelin quantification [version 1; peer review: 1 not approved]
dc.typeArticle

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