Myelin detection in fluorescence microscopy images using machine learning


Çimen Yetiş S., Çapar A., Ekinci D. A., Ayten U. E., Kerman B. E., Töreyin B. U.

JOURNAL OF NEUROSCIENCE METHODS, vol.346, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 346
  • Publication Date: 2020
  • Doi Number: 10.1016/j.jneumeth.2020.108946
  • Journal Name: JOURNAL OF NEUROSCIENCE METHODS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, EMBASE, MEDLINE, Veterinary Science Database
  • Keywords: Myelin detection, Fluorescence image analysis, Machine learning, Supervised learning, Deep learning, Myelin quantification, MULTIPLE, REMYELINATION, SEGMENTATION, TOOL
  • Yıldız Technical University Affiliated: Yes

Abstract

Background: The myelin sheath produced by glial cells insulates the axons, and supports the function of the nervous system. Myelin sheath degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS). There are no therapies for MS that promote remyelination. Drug discovery frequently involves screening thousands of compounds. However, this is not feasible for remyelination drugs, since myelin quantification is a manual labor-intensive endeavor. Therefore, the development of assistive software for expedited myelin detection is instrumental for MS drug discovery by enabling high-content image-based drug screens.