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Spinal cord gray matter segmentation using deep dilated convolutions
Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and were recently found relevant as a biomarker for disability in amyotrophic lateral sclerosis. The ability to automatically segment the GM is, therefore, an important task for modern studies of the spi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5899127/ https://www.ncbi.nlm.nih.gov/pubmed/29654236 http://dx.doi.org/10.1038/s41598-018-24304-3 |
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author | Perone, Christian S. Calabrese, Evan Cohen-Adad, Julien |
author_facet | Perone, Christian S. Calabrese, Evan Cohen-Adad, Julien |
author_sort | Perone, Christian S. |
collection | PubMed |
description | Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and were recently found relevant as a biomarker for disability in amyotrophic lateral sclerosis. The ability to automatically segment the GM is, therefore, an important task for modern studies of the spinal cord. In this work, we devise a modern, simple and end-to-end fully-automated human spinal cord gray matter segmentation method using Deep Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate our method against six independently developed methods on a GM segmentation challenge. We report state-of-the-art results in 8 out of 10 evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets. |
format | Online Article Text |
id | pubmed-5899127 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-58991272018-04-20 Spinal cord gray matter segmentation using deep dilated convolutions Perone, Christian S. Calabrese, Evan Cohen-Adad, Julien Sci Rep Article Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and were recently found relevant as a biomarker for disability in amyotrophic lateral sclerosis. The ability to automatically segment the GM is, therefore, an important task for modern studies of the spinal cord. In this work, we devise a modern, simple and end-to-end fully-automated human spinal cord gray matter segmentation method using Deep Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate our method against six independently developed methods on a GM segmentation challenge. We report state-of-the-art results in 8 out of 10 evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets. Nature Publishing Group UK 2018-04-13 /pmc/articles/PMC5899127/ /pubmed/29654236 http://dx.doi.org/10.1038/s41598-018-24304-3 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Perone, Christian S. Calabrese, Evan Cohen-Adad, Julien Spinal cord gray matter segmentation using deep dilated convolutions |
title | Spinal cord gray matter segmentation using deep dilated convolutions |
title_full | Spinal cord gray matter segmentation using deep dilated convolutions |
title_fullStr | Spinal cord gray matter segmentation using deep dilated convolutions |
title_full_unstemmed | Spinal cord gray matter segmentation using deep dilated convolutions |
title_short | Spinal cord gray matter segmentation using deep dilated convolutions |
title_sort | spinal cord gray matter segmentation using deep dilated convolutions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5899127/ https://www.ncbi.nlm.nih.gov/pubmed/29654236 http://dx.doi.org/10.1038/s41598-018-24304-3 |
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