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Myelin detection in fluorescence microscopy images using machine learning.

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. 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.

journal_name

J Neurosci Methods

journal_title

Journal of neuroscience methods

authors

Çimen Yetiş S,Çapar A,Ekinci DA,Ayten UE,Kerman BE,Töreyin BU

doi

10.1016/j.jneumeth.2020.108946

subject

Has Abstract

pub_date

2020-12-01 00:00:00

pages

108946

eissn

0165-0270

issn

1872-678X

pii

S0165-0270(20)30369-1

journal_volume

346

pub_type

杂志文章

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Myelin detection in fluorescence microscopy images using machine learning.