Author:
Daeschler Simeon Christian,Bourget Marie-Hélène,Derakhshan Dorsa,Sharma Vasudev,Asenov Stoyan Ivaylov,Gordon Tessa,Cohen-Adad Julien,Borschel Gregory Howard
Abstract
AbstractWe aimed to develop and validate a deep learning model for automated segmentation and histomorphometry of myelinated peripheral nerve fibers from light microscopic images. A convolutional neural network integrated in the AxonDeepSeg framework was trained for automated axon/myelin segmentation using a dataset of light-microscopic cross-sectional images of osmium tetroxide-stained rat nerves including various axonal regeneration stages. In a second dataset, accuracy of automated segmentation was determined against manual axon/myelin labels. Automated morphometry results, including axon diameter, myelin sheath thickness and g-ratio were compared against manual straight-line measurements and morphometrics extracted from manual labels with AxonDeepSeg as a reference standard. The neural network achieved high pixel-wise accuracy for nerve fiber segmentations with a mean (± standard deviation) ground truth overlap of 0.93 (± 0.03) for axons and 0.99 (± 0.01) for myelin sheaths, respectively. Nerve fibers were identified with a sensitivity of 0.99 and a precision of 0.97. For each nerve fiber, the myelin thickness, axon diameter, g-ratio, solidity, eccentricity, orientation, and individual x -and y-coordinates were determined automatically. Compared to manual morphometry, automated histomorphometry showed superior agreement with the reference standard while reducing the analysis time to below 2.5% of the time needed for manual morphometry. This open-source convolutional neural network provides rapid and accurate morphometry of entire peripheral nerve cross-sections. Given its easy applicability, it could contribute to significant time savings in biomedical research while extracting unprecedented amounts of objective morphologic information from large image datasets.
Funder
Deutsche Forschungsgemeinschaft
Fonds de Recherche du Québec - Santé
Canada First Research Excellence Fund
Canada Research Chair in Quantitative Magnetic Resonance Imaging
Canadian Institutes of Health Research
Canada Foundation for Innovation
Natural Sciences and Engineering Research Council of Canada
Courtois NeuroMod project and the Quebec BioImaging Network
Publisher
Springer Science and Business Media LLC
Cited by
10 articles.
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