Myelin detection in fluorescence microscopy images using machine learning

ÇİMEN 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
  • Istanbul Medipol University Affiliated: Yes


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. New method: In this study, we developed a machine learning based expedited myelin detection approach in fluorescence microscopy images. Multi-channel three-dimensional microscopy images of a mouse stem cell-based myelination assay were labeled by experts. A spectro-spatial feature extraction method was introduced to represent local dependencies of voxels both in spatial and spectral domains. Feature extraction yielded two data set of over forty-seven thousand annotated images in total. Results: Myelin detection performances of 23 different supervised machine learning techniques including a customized-convolutional neural network (CNN), were assessed using various train/test split ratios of the data sets. The highest accuracy values of 98.84±0.09% and 98.46±0.11% were achieved by Boosted Trees and customized-CNN, respectively. Comparison with existing methods: Our approach can detect myelin in a common experimental setup. Myelin extending in any orientation in 3 dimensions is segmented from 3 channel z-stack fluorescence images. Conclusions: Our results suggest that the proposed expedited myelin detection approach is a feasible and robust method for remyelination drug screening.