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.
A multi-spectral myelin annotation tool for machine learning based myelin quantification.
阅读:5
作者:Ãapar Abdulkerim, Ãimen Sibel, AladaÄ Zeynep, Ekinci Dursun Ali, Ayten Umut Engin, Kerman Bilal Ersen, Töreyin Behçet UÄur
| 期刊: | F1000Research | 影响因子: | 0.000 |
| 时间: | 2020 | 起止号: | 2023 Nov 15; 9:1492 |
| doi: | 10.12688/f1000research.27139.4 | ||
特别声明
1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。
2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。
3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。
4、投稿及合作请联系:info@biocloudy.com。
