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一种基于机器学习的多发性硬化斑块标记的多光谱标记工具。

A multi-spectral myelin annotation tool for machine learning based myelin quantification.

机构信息

Informatics Institute, Istanbul Technical University, Istanbul, 34469, Turkey.

Argenit Akıllı Bilgi Teknolojileri, Istanbul, 34469, Turkey.

出版信息

F1000Res. 2023 Nov 15;9:1492. doi: 10.12688/f1000research.27139.4. eCollection 2020.

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.

摘要

髓鞘是神经系统的重要组成部分,髓鞘损伤会导致脱髓鞘疾病。髓鞘是包裹在神经元轴突上的一层少突胶质细胞膜。在荧光图像中,专家通过对符合一定形状和大小标准的少突胶质细胞和轴突膜进行共定位来手动识别髓鞘。由于髓鞘在 x-y-z 轴上蠕动,因此机器学习非常适合其分割。然而,机器学习方法,特别是卷积神经网络(CNN),需要大量的注释图像,这需要专家的劳动。为了便于髓鞘注释,我们开发了一种从多光谱荧光图像中提取髓鞘真实数据的工作流程和软件。此外,据我们所知,这是首次为机器学习应用共享一组经过注释的髓鞘真实数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07e2/10660345/5a53a360debf/f1000research-9-158759-g0000.jpg

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