{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/axondeepseg-automatic-axon-and-myelin","title":"AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks","arxiv_id":"1711.01004","date":"2017-11-03","proceeding":"Nature Scientific Reports 2018 2","authors":["Aldo Zaimi","Maxime Wabartha","Victor Herman","Pierre-Louis Antonsanti","Christian Samuel Perone","Julien Cohen-Adad"],"abstract":"Segmentation of axon and myelin from microscopy images of the nervous system\nprovides useful quantitative information about the tissue microstructure, such\nas axon density and myelin thickness. This could be used for instance to\ndocument cell morphometry across species, or to validate novel non-invasive\nquantitative magnetic resonance imaging techniques. Most currently-available\nsegmentation algorithms are based on standard image processing and usually\nrequire multiple processing steps and/or parameter tuning by the user to adapt\nto different modalities. Moreover, only few methods are publicly available. We\nintroduce AxonDeepSeg, an open-source software that performs axon and myelin\nsegmentation of microscopic images using deep learning. AxonDeepSeg features:\n(i) a convolutional neural network architecture; (ii) an easy training\nprocedure to generate new models based on manually-labelled data and (iii) two\nready-to-use models trained from scanning electron microscopy (SEM) and\ntransmission electron microscopy (TEM). Results show high pixel-wise accuracy\nacross various species: 85% on rat SEM, 81% on human SEM, 95% on mice TEM and\n84% on macaque TEM. Segmentation of a full rat spinal cord slice is computed\nand morphological metrics are extracted and compared against the literature.\nAxonDeepSeg is freely available at https://github.com/neuropoly/axondeepseg","url_abs":"http://arxiv.org/abs/1711.01004v2","url_pdf":"http://arxiv.org/pdf/1711.01004v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"axondeepseg-automatic-axon-and-myelin","repo_url":"https://github.com/neuropoly/axondeepseg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}