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Abstract
We 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.
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1 SickKids Research Institute, Neuroscience and Mental Health Program, Hospital for Sick Children (SickKids), Toronto, Canada (GRID:grid.42327.30) (ISNI:0000 0004 0473 9646)
2 Polytechnique Montreal, NeuroPoly Laboratory, Institute of Biomedical Engineering, Montreal, Canada (GRID:grid.183158.6) (ISNI:0000 0004 0435 3292)
3 University of Toronto, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938)
4 Polytechnique Montreal, NeuroPoly Laboratory, Institute of Biomedical Engineering, Montreal, Canada (GRID:grid.183158.6) (ISNI:0000 0004 0435 3292); University of Toronto, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938)
5 SickKids Research Institute, Neuroscience and Mental Health Program, Hospital for Sick Children (SickKids), Toronto, Canada (GRID:grid.42327.30) (ISNI:0000 0004 0473 9646); the Hospital for Sick Children, Division of Plastic and Reconstructive Surgery, Toronto, Canada (GRID:grid.42327.30) (ISNI:0000 0004 0473 9646)
6 Polytechnique Montreal, NeuroPoly Laboratory, Institute of Biomedical Engineering, Montreal, Canada (GRID:grid.183158.6) (ISNI:0000 0004 0435 3292); CRIUGM, University of Montreal, Functional Neuroimaging Unit, Montreal, Canada (GRID:grid.14848.31) (ISNI:0000 0001 2292 3357); Mila - Quebec AI Institute, Montreal, Canada (GRID:grid.14848.31)
7 SickKids Research Institute, Neuroscience and Mental Health Program, Hospital for Sick Children (SickKids), Toronto, Canada (GRID:grid.42327.30) (ISNI:0000 0004 0473 9646); University of Toronto, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); the Hospital for Sick Children, Division of Plastic and Reconstructive Surgery, Toronto, Canada (GRID:grid.42327.30) (ISNI:0000 0004 0473 9646); Indiana University School of Medicine, Indianapolis, USA (GRID:grid.257413.6) (ISNI:0000 0001 2287 3919)