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


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Çapar A., Yetiş S. Ç. , Aladağ Z., Ekinci D. A. , AYTEN U. E. , Kerman B. E. , ...More

F1000Research, vol.9, pp.1-10, 2021 (Refereed Journals of Other Institutions) identifier

  • Publication Type: Article / Article
  • Volume: 9
  • Publication Date: 2021
  • Doi Number: 10.12688/f1000research.27139.1
  • Title of Journal : F1000Research
  • Page Numbers: pp.1-10
  • Keywords: fluorescence images, image analysis, machine learning, myelin annotation tool, myelin quantification

Abstract

© 2020. Çapar A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citedMyelin 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