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 necessitates expert labor. To facilitate myelin annotation, we developed a workflow and a software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, we shared a set of myelin ground truths annotated using this workflow.
Highlights
Myelin degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS)[1,2]
In this study, myelin was marked by two experts on previously acquired oligodendrocyte and neuron co-culture images[5] using the described workflow
The ground truth images were saved as TIF on CEM3D6
Summary
Myelin degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS)[1,2]. Myelin quantification is essential for drug discovery, which often involves screening thousands of compounds[3]. Automation of quantification using machine learning can facilitate drug discovery by reducing time and labor costs. Myelin annotation suffers the same limitations as manual quantification. To assist researchers and bioimage analysts, we developed a workflow and a software for myelin ground truth extraction from multi-spectral fluorescent images
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