| Autor: |
Abdulkerim Çapar, Dursun Ali Ekinci, Umut Engin Ayten, Sibel Çimen, Zeynep Aladağ, Behçet Uğur Töreyin, Bilal Ersen Kerman |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
F1000Research, Vol 9 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2046-1402 |
| DOI: |
10.12688/f1000research.27139.4 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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