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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 et Julien Cohen-Adad

Article de revue (2018)

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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 .

Mots clés

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

Département: Institut de génie biomédical
Centre de recherche: NeuroPoly - Laboratoire de Recherche en Neuroimagerie
Organismes subventionnaires: 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
Numéro de subvention: CIHR FDN-143263, 32454, 28826, 2015-PR-182754, 435897-2013, 34824
URL de PolyPublie: https://publications.polymtl.ca/5187/
Titre de la revue: Scientific Reports (vol. 8, no 1)
Maison d'édition: Nature
DOI: 10.1038/s41598-018-22181-4
URL officielle: https://doi.org/10.1038/s41598-018-22181-4
Date du dépôt: 03 juin 2022 14:40
Dernière modification: 23 mai 2023 12:49
Citer en 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

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