Daeschler, Simeon ChristianBourget, Marie-HélèneDerakhshan, DorsaSharma, VasudevAsenov, Stoyan IvaylovGordon, TessaCohen-Adad, JulienBorschel , Gregory Howard2024-05-152024-05-152022-04-08Daeschler, S. C., Bourget, M.-H., Derakhshan, D., Sharma, V., Asenov, S. I., Gordon, T., Cohen-Adad, J., & Borschel, G. H. (2022). Rapid, automated nerve histomorphometry through open-source artificial intelligence. Scientific Reports, 12(1), 5975. https://doi.org/10.1038/s41598-022-10066-6https://hdl.handle.net/1805/40782We 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.en-USAttribution 4.0 InternationalPeripheral nervous systemRegeneration and repair in the nervous systemArticle