Alison L. Wong, Nicholas Hricz, Harsha Malapati, Nicholas von Guionneau, Michael Wong, Thomas Harris, Mathieu Boudreau, Julien Cohen-Adad et Sami Tuffaha
Article de revue (2021)
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Abstract
Background Manual axon histomorphometry (AH) is time- and resource-intensive, which has inspired many attempts at automation. However, there has been little investigation on implementation of automated programs for widespread use. Ideally such a program should be able to perform AH across imaging modalities and nerve states. AxonDeepSeg (ADS) is an open source deep learning program that has previously been validated in electron microscopy. We evaluated the robustness of ADS for peripheral nerve axonal histomorphometry in light micrographs prepared using two different methods.
Methods Axon histomorphometry using ADS and manual analysis (gold-standard) was performed on light micrographs of naïve or regenerating rat median nerve cross-sections prepared with either toluidine-resin or osmium-paraffin embedding protocols. The parameters of interest included axon count, axon diameter, myelin thickness, and g-ratio.
Results Manual and automatic ADS axon counts demonstrated good agreement in naïve nerves and moderate agreement on regenerating nerves. There were small but consistent differences in measured axon diameter, myelin thickness and g-ratio; however, absolute differences were small. Both methods appropriately identified differences between naïve and regenerating nerves. ADS was faster than manual axon analysis.
Conclusions Without any algorithm retraining, ADS was able to appropriately identify critical differences between naïve and regenerating nerves and work with different sample preparation methods of peripheral nerve light micrographs. While there were differences between absolute values between manual and ADS, ADS performed consistently and required much less time. ADS is an accessible and robust tool for AH that can provide consistent analysis across protocols and nerve states.
Renseignements supplémentaires: | Data Availability Statement: All files are available from the Harvard Dataverse database (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/H9N9ZU). |
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Sujet(s): |
1900 Génie biomédical > 1900 Génie biomédical 1900 Génie biomédical > 1901 Technologie biomédicale |
Département: | Institut de génie biomédical |
Centre de recherche: | NeuroPoly - Laboratoire de Recherche en Neuroimagerie |
URL de PolyPublie: | https://publications.polymtl.ca/9360/ |
Titre de la revue: | PLOS One (vol. 16, no 7) |
Maison d'édition: | PLOS |
DOI: | 10.1371/journal.pone.0248323 |
URL officielle: | https://doi.org/10.1371/journal.pone.0248323 |
Date du dépôt: | 16 août 2023 11:27 |
Dernière modification: | 08 avr. 2024 15:28 |
Citer en APA 7: | Wong, A. L., Hricz, N., Malapati, H., von Guionneau, N., Wong, M., Harris, T., Boudreau, M., Cohen-Adad, J., & Tuffaha, S. (2021). A simple and robust method for automating analysis of naïve and regenerating peripheral nerves. PLOS One, 16(7), 14 pages. https://doi.org/10.1371/journal.pone.0248323 |
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