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

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Çapar A., Çimen S., Aladağ Z., Ekinci D. A., Ayten U. E., Kerman B. E., ...Daha Fazla

F1000Research, cilt.9, ss.1492, 2020 (Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9
  • Basım Tarihi: 2020
  • Doi Numarası: 10.12688/f1000research.27139.4
  • Dergi Adı: F1000Research
  • Derginin Tarandığı İndeksler: Scopus, EMBASE, MEDLINE, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1492
  • Anahtar Kelimeler: fluorescence images, image analysis, machine learning, myelin annotation tool, myelin quantification
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

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.