A multi-spectral myelin annotation tool for machine learning based myelin quantification
F1000Research, cilt.9, ss.1492, 2020 (Scopus)
- 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
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- 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.