ArticleScientific reports2022
Rapid, automated nerve histomorphometry through open-source artificial intelligence.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- AxoDetect: an automated nerve image segmentation and quantification workflow for computational nerve modeling.Journal of neural engineering · 2024Article
- Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges.Neural regeneration research · 2024Article
- Artificial Intelligence in Scoliosis Classification: An Investigation of Language-Based Models.Journal of personalized medicine · 2023Article
- Development and preclinical evaluation of bioactive nerve conduits for peripheral nerve regeneration: A comparative study.Materials today. Bio · 2023Article
- Increased Nerve Density Adversely Affects Outcome in Oral Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
- Evaluating Scoliosis Severity Based on Posturographic X-ray Images Using a Contrastive Language-Image Pretraining Model.Diagnostics (Basel, Switzerland) · 2023Article
- Morphological Methods to Evaluate Peripheral Nerve Fiber Regeneration: A Comprehensive Review.International journal of molecular sciences · 2023Review
- Automated pipeline for nerve fiber selection and g-ratio calculation in optical microscopy: exploring staining protocol variations.Frontiers in neuroanatomy · 2023Article
- Foretinib mitigates cutaneous nerve fiber loss in experimental diabetic neuropathy.Scientific reports · 2022Article
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8 authors.
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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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