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AxonSeg: Open Source Software for Axon and Myelin Segmentation and Morphometric Analysis
Segmenting axon and myelin from microscopic images is relevant for studying the peripheral and central nervous system and for validating new MRI techniques that aim at quantifying tissue microstructure. While several software packages have been proposed, their interface is sometimes limited and/or t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990549/ https://www.ncbi.nlm.nih.gov/pubmed/27594833 http://dx.doi.org/10.3389/fninf.2016.00037 |
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author | Zaimi, Aldo Duval, Tanguy Gasecka, Alicja Côté, Daniel Stikov, Nikola Cohen-Adad, Julien |
author_facet | Zaimi, Aldo Duval, Tanguy Gasecka, Alicja Côté, Daniel Stikov, Nikola Cohen-Adad, Julien |
author_sort | Zaimi, Aldo |
collection | PubMed |
description | Segmenting axon and myelin from microscopic images is relevant for studying the peripheral and central nervous system and for validating new MRI techniques that aim at quantifying tissue microstructure. While several software packages have been proposed, their interface is sometimes limited and/or they are designed to work with a specific modality (e.g., scanning electron microscopy (SEM) only). Here we introduce AxonSeg, which allows to perform automatic axon and myelin segmentation on histology images, and to extract relevant morphometric information, such as axon diameter distribution, axon density and the myelin g-ratio. AxonSeg includes a simple and intuitive MATLAB-based graphical user interface (GUI) and can easily be adapted to a variety of imaging modalities. The main steps of AxonSeg consist of: (i) image pre-processing; (ii) pre-segmentation of axons over a cropped image and discriminant analysis (DA) to select the best parameters based on axon shape and intensity information; (iii) automatic axon and myelin segmentation over the full image; and (iv) atlas-based statistics to extract morphometric information. Segmentation results from standard optical microscopy (OM), SEM and coherent anti-Stokes Raman scattering (CARS) microscopy are presented, along with validation against manual segmentations. Being fully-automatic after a quick manual intervention on a cropped image, we believe AxonSeg will be useful to researchers interested in large throughput histology. AxonSeg is open source and freely available at: https://github.com/neuropoly/axonseg. |
format | Online Article Text |
id | pubmed-4990549 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-49905492016-09-02 AxonSeg: Open Source Software for Axon and Myelin Segmentation and Morphometric Analysis Zaimi, Aldo Duval, Tanguy Gasecka, Alicja Côté, Daniel Stikov, Nikola Cohen-Adad, Julien Front Neuroinform Neuroscience Segmenting axon and myelin from microscopic images is relevant for studying the peripheral and central nervous system and for validating new MRI techniques that aim at quantifying tissue microstructure. While several software packages have been proposed, their interface is sometimes limited and/or they are designed to work with a specific modality (e.g., scanning electron microscopy (SEM) only). Here we introduce AxonSeg, which allows to perform automatic axon and myelin segmentation on histology images, and to extract relevant morphometric information, such as axon diameter distribution, axon density and the myelin g-ratio. AxonSeg includes a simple and intuitive MATLAB-based graphical user interface (GUI) and can easily be adapted to a variety of imaging modalities. The main steps of AxonSeg consist of: (i) image pre-processing; (ii) pre-segmentation of axons over a cropped image and discriminant analysis (DA) to select the best parameters based on axon shape and intensity information; (iii) automatic axon and myelin segmentation over the full image; and (iv) atlas-based statistics to extract morphometric information. Segmentation results from standard optical microscopy (OM), SEM and coherent anti-Stokes Raman scattering (CARS) microscopy are presented, along with validation against manual segmentations. Being fully-automatic after a quick manual intervention on a cropped image, we believe AxonSeg will be useful to researchers interested in large throughput histology. AxonSeg is open source and freely available at: https://github.com/neuropoly/axonseg. Frontiers Media S.A. 2016-08-19 /pmc/articles/PMC4990549/ /pubmed/27594833 http://dx.doi.org/10.3389/fninf.2016.00037 Text en Copyright © 2016 Zaimi, Duval, Gasecka, Côté, Stikov and Cohen-Adad. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Zaimi, Aldo Duval, Tanguy Gasecka, Alicja Côté, Daniel Stikov, Nikola Cohen-Adad, Julien AxonSeg: Open Source Software for Axon and Myelin Segmentation and Morphometric Analysis |
title | AxonSeg: Open Source Software for Axon and Myelin Segmentation and Morphometric Analysis |
title_full | AxonSeg: Open Source Software for Axon and Myelin Segmentation and Morphometric Analysis |
title_fullStr | AxonSeg: Open Source Software for Axon and Myelin Segmentation and Morphometric Analysis |
title_full_unstemmed | AxonSeg: Open Source Software for Axon and Myelin Segmentation and Morphometric Analysis |
title_short | AxonSeg: Open Source Software for Axon and Myelin Segmentation and Morphometric Analysis |
title_sort | axonseg: open source software for axon and myelin segmentation and morphometric analysis |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990549/ https://www.ncbi.nlm.nih.gov/pubmed/27594833 http://dx.doi.org/10.3389/fninf.2016.00037 |
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