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A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 2; peer review: 1 approved]

Authors :
Abdulkerim Çapar
Sibel Çimen
Zeynep Aladağ
Dursun Ali Ekinci
Umut Engin Ayten
Bilal Ersen Kerman
Behçet Uğur Töreyin
Author Affiliations :
<relatesTo>1</relatesTo>Informatics Institute, Istanbul Technical University, Istanbul, 34469, Turkey<br /><relatesTo>2</relatesTo>Argenit Smart Information Technologies, Istanbul, 34469, Turkey<br /><relatesTo>3</relatesTo>Department of Electronics and Communication Engineering, Yildiz Technical University, Istanbul, 34220, Turkey<br /><relatesTo>4</relatesTo>Regenerative and Restorative Medicine Research Center, Istanbul Medipol University, Istanbul, 34810, Turkey<br /><relatesTo>5</relatesTo>School of Medicine Department of Histology and Embryology, Istanbul Medipol University, Istanbul, 34810, Turkey
Source :
F1000Research. 9:1492
Publication Year :
2022
Publisher :
London, UK: F1000 Research Limited, 2022.

Abstract

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.

Details

ISSN :
20461402
Volume :
9
Database :
F1000Research
Journal :
F1000Research
Notes :
Revised Amendments from Version 1 The differences between CEM and CEMotates tools are clearly explained in the new version of the text. The advantages of CEMotate, which was developed by giving the time metrics of the tools and the precision differences of the experts, was indicated by numerical data., , [version 2; peer review: 1 approved]
Publication Type :
Academic Journal
Accession number :
edsfor.10.12688.f1000research.27139.2
Document Type :
software-tool
Full Text :
https://doi.org/10.12688/f1000research.27139.2