<  Back to the Polytechnique Montréal portal

AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

Aldo Zaimi, Maxime Wabartha, Victor Herman, Pierre-Louis Antonsanti, Christian S. Perone, Julien Cohen-Adad

Article (2018)

Open Acess document in PolyPublie and at official publisher
[img]
Preview
Open Access to the full text of this document
Published Version
Terms of Use: Creative Commons Attribution
Download (1MB)
Show abstract
Hide abstract

Abstract

Segmentation of axon and myelin from microscopy images of the nervous system provides useful quantitative information about the tissue microstructure, such as axon density and myelin thickness. This could be used for instance to document cell morphometry across species, or to validate novel non-invasive quantitative magnetic resonance imaging techniques. Most currently-available segmentation algorithms are based on standard image processing and usually require multiple processing steps and/or parameter tuning by the user to adapt to different modalities. Moreover, only a few methods are publicly available. We introduce AxonDeepSeg, an open-source software that performs axon and myelin segmentation of microscopic images using deep learning. AxonDeepSeg features: (i) a convolutional neural network architecture; (ii) an easy training procedure to generate new models based on manually-labelled data and (iii) two ready-to-use models trained from scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Results show high pixel-wise accuracy across various species: 85% on rat SEM, 81% on human SEM, 95% on mice TEM and 84% on macaque TEM. Segmentation of a full rat spinal cord slice is computed and morphological metrics are extracted and compared against the literature. AxonDeepSeg is freely available at https://github.com/neuropoly/axondeepseg .

Uncontrolled Keywords

Animals; Automation; Axons/*metabolism; Image Processing, Computer-Assisted/*methods; Mice; *Microscopy; Myelin Sheath/*metabolism; *Neural Networks, Computer; *Software

Department: Institut de génie biomédical
Research Center: NeuroPoly - Laboratoire de Recherche en Neuroimagerie
Funders: Canada Research Chair in Quantitative Magnetic Resonance Imaging (JCA), Canadian Institute of Health Research, Canada Foundation for Innovation, Fonds de Recherche du Québec - Santé, Fonds de Recherche du Québec - Nature et Technologies, Natural Sciences and Engineering Research Council of Canada, IVADO, TransMedTech, Quebec BioImaging Network
Grant number: CIHR FDN-143263, 32454, 28826, 2015-PR-182754, 435897-2013, 34824
PolyPublie URL: https://publications.polymtl.ca/5187/
Journal Title: Scientific Reports (vol. 8, no. 1)
Publisher: Nature
DOI: 10.1038/s41598-018-22181-4
Official URL: https://doi.org/10.1038/s41598-018-22181-4
Date Deposited: 03 Jun 2022 14:40
Last Modified: 08 Nov 2022 15:00
Cite in APA 7: Zaimi, A., Wabartha, M., Herman, V., Antonsanti, P.-L., Perone, C. S., & Cohen-Adad, J. (2018). AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks. Scientific Reports, 8(1), 3816 (11 pages). https://doi.org/10.1038/s41598-018-22181-4

Statistics

Total downloads

Downloads per month in the last year

Origin of downloads

Dimensions

Repository Staff Only

View Item View Item