dc.contributor.author | Çapar, Abdulkerim | |
dc.contributor.author | Çimen Yetiş, Sibel | |
dc.contributor.author | Aladağ, Zeynep | |
dc.contributor.author | Ekinci, Dursun Ali | |
dc.contributor.author | Ayten, Umut Engin | |
dc.contributor.author | Kerman, Bilal Ersen | |
dc.contributor.author | Töreyin, Behçet Uğur | |
dc.date.accessioned | 2021-11-01T07:25:43Z | |
dc.date.available | 2021-11-01T07:25:43Z | |
dc.date.issued | 2021 | en_US |
dc.identifier.citation | Çapar, 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 | en_US |
dc.identifier.issn | 2046-1402 | |
dc.identifier.uri | https://dx.doi.org/10.12688/F1000RESEARCH.27139.1 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12511/8539 | |
dc.description.abstract | Myelin 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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | F1000 Research Ltd | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Fluorescence Images | en_US |
dc.subject | Image Analysis | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Myelin Annotation Tool | en_US |
dc.subject | Myelin Quantification | en_US |
dc.title | A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 1; peer review: 1 not approved] | en_US |
dc.type | article | en_US |
dc.relation.ispartof | F1000Research | en_US |
dc.department | İstanbul Medipol Üniversitesi, Rektörlük, Rejeneratif ve Restoratif Tıp Araştırmaları Merkezi (REMER) | en_US |
dc.department | İstanbul Medipol Üniversitesi, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü, Histoloji ve Embriyoloji Ana Bilim Dalı | en_US |
dc.authorid | 0000-0003-1106-3288 | en_US |
dc.identifier.volume | 9 | en_US |
dc.identifier.startpage | 1 | en_US |
dc.identifier.endpage | 10 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.12688/F1000RESEARCH.27139.1 | en_US |
dc.identifier.scopusquality | Q1 | en_US |