Morphometric analysis of peripheral myelinated nerve fibers through deep learning.

Irrespective of initial causes of neurological diseases, these disorders usually exhibit two key pathological changes-axonal loss or demyelination or a mixture of the two. Therefore, vigorous quantification of myelin and axons is essential in studying these diseases. However, the process of quantification has been labor intensive and time-consuming because of the requisite manual segmentation of myelin and axons from microscopic nerve images. As a part of AI development, deep learning has been utilized to automate certain tasks, such as image analysis. This study describes the development of a convolutional neural network (CNN)-based approach to segment images of mouse nerve cross sections. We adapted the U-Net architecture and used manually-produced segmentation data accumulated over many years in our lab for training. These images ranged from normal nerves to those afflicted by severe myelin and axon pathologies; thus, maximizing the trained model's ability to recognize atypical myelin structures. Morphometric data produced by applying the trained model to additional images were then compared to manually obtained morphometrics. The former effectively shortened the time consumption in the morphometric analysis with excellent accuracy in axonal density and g-ratio. However, we were not able to completely eliminate manual refinement of the segmentation product. We also observed small variations in axon diameter and myelin thickness within 9.5%. Nevertheless, we learned alternative ways to improve accuracy through the study. Overall, greatly increased efficiency in the CNN-based approach out-weighs minor limitations that will be addressed in future studies, thus justifying our confidence in its prospects. Note: All the relevant code is freely available at https://neurology.med.wayne.edu/drli-datashairing.