Myelin detection in fluorescence microscopy images using machine learning.

• Myelin damage is at the heart of many neurodegenerative diseases such as MS. • Drug discovery for MS requires manual myelin counting on the microscopic images. • Myelin detection may be expedited by orders of magnitude using machine learning. • Myelin detection performances of 23 machine learning techniques were evaluated. • Boosted Trees and customized-CNN were accurate (over 98%), robust, and fast. The myelin sheath produced by glial cells insulates the axons, and supports the function of the nervous system. Myelin sheath degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS). There are no therapies for MS that promote remyelination. Drug discovery frequently involves screening thousands of compounds. However, this is not feasible for remyelination drugs, since myelin quantification is a manual labor-intensive endeavor. Therefore, the development of assistive software for expedited myelin detection is instrumental for MS drug discovery by enabling high-content image-based drug screens. In this study, we developed a machine learning based expedited myelin detection approach in fluorescence microscopy images. Multi-channel three-dimensional microscopy images of a mouse stem cell-based myelination assay were labeled by experts. A spectro-spatial feature extraction method was introduced to represent local dependencies of voxels both in spatial and spectral domains. Feature extraction yielded two data set of over forty-seven thousand annotated images in total. Myelin detection performances of 23 different supervised machine learning techniques including a customized-convolutional neural network (CNN), were assessed using various train/test split ratios of the data sets. The highest accuracy values of 98.84 ± 0.09 % and 98.46 ± 0.11 % were achieved by Boosted Trees and customized-CNN, respectively. Our approach can detect myelin in a common experimental setup. Myelin extending in any orientation in 3 dimensions is segmented from 3 channel z-stack fluorescence images. Our results suggest that the proposed expedited myelin detection approach is a feasible and robust method for remyelination drug screening. [ABSTRACT FROM AUTHOR]