Rapid, automated nerve histomorphometry through open-source artificial intelligence

Simeon C. Daeschler

doi:10.1038/s41598-022-10066-6

2022-04-08

Simeon C. Daeschler *

Marie-Hélène Bourget

Vasudev Omprakash Sharma

Stoyan Ivaylov Asenov

Tessa Gordon

Julien Cohen‐Adad

Gregory H. Borschel

DOI：10.1038/s41598-022-10066-6

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摘要

En 中文

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.

Keyword:

PERIPHERAL-NERVE

FIBERS

MORPHOMETRY

DENSITY

AXONS