A multi-spectral myelin annotation tool for machine learning based myelin quantification


Çapar A., ÇİMEN S., Aladağ Z., Ekinci D. A., AYTEN U. E., KERMAN B. E., ...More

F1000Research, vol.9, pp.1492, 2020 (Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9
  • Publication Date: 2020
  • Doi Number: 10.12688/f1000research.27139.4
  • Journal Name: F1000Research
  • Journal Indexes: Scopus, EMBASE, MEDLINE, Directory of Open Access Journals
  • Page Numbers: pp.1492
  • Keywords: fluorescence images, image analysis, machine learning, myelin annotation tool, myelin quantification
  • Istanbul Technical University Affiliated: Yes

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 co-localization 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, machine-learning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitate expert labor. To facilitate myelin annotation, we developed a workflow and software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, to the best of our knowledge, for the first time, a set of annotated myelin ground truths for machine learning applications were shared with the community.