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Brain MRI dataset of multiple sclerosis with consensus manual lesion segmentation and patient meta information

Magnetic resonance imaging (MRI) provides a significant key to diagnose and monitor the progression of multiple sclerosis (MS) disease. Manual MS-lesion segmentation, expanded disability status scale (EDSS) and patient's meta information can provide a gold standard for research in terms of auto...

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Autores principales: Muslim, Ali M., Mashohor, Syamsiah, Gawwam, Gheyath Al, Mahmud, Rozi, Hanafi, Marsyita binti, Alnuaimi, Osama, Josephine, Raad, Almutairi, Abdullah Dhaifallah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9043670/
https://www.ncbi.nlm.nih.gov/pubmed/35496484
http://dx.doi.org/10.1016/j.dib.2022.108139
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author Muslim, Ali M.
Mashohor, Syamsiah
Gawwam, Gheyath Al
Mahmud, Rozi
Hanafi, Marsyita binti
Alnuaimi, Osama
Josephine, Raad
Almutairi, Abdullah Dhaifallah
author_facet Muslim, Ali M.
Mashohor, Syamsiah
Gawwam, Gheyath Al
Mahmud, Rozi
Hanafi, Marsyita binti
Alnuaimi, Osama
Josephine, Raad
Almutairi, Abdullah Dhaifallah
author_sort Muslim, Ali M.
collection PubMed
description Magnetic resonance imaging (MRI) provides a significant key to diagnose and monitor the progression of multiple sclerosis (MS) disease. Manual MS-lesion segmentation, expanded disability status scale (EDSS) and patient's meta information can provide a gold standard for research in terms of automated MS-lesion quantification, automated EDSS prediction and identification of the correlation between MS-lesion and patient disability. In this dataset, we provide a novel multi-sequence MRI dataset of 60 MS patients with consensus manual lesion segmentation, EDSS, general patient information and clinical information. On this dataset, three radiologists and neurologist experts segmented and validated the manual MS-lesion segmentation for three MRI sequences T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR). The dataset can be used to study the relationship between MS-lesion, EDSS and patient clinical information. Furthermore, it also can be used for the development of automated MS-lesion segmentation, patient disability prediction using MRI and correlation analysis between patient disability and MRI brain abnormalities include MS lesion location, size, number and type.
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spelling pubmed-90436702022-04-28 Brain MRI dataset of multiple sclerosis with consensus manual lesion segmentation and patient meta information Muslim, Ali M. Mashohor, Syamsiah Gawwam, Gheyath Al Mahmud, Rozi Hanafi, Marsyita binti Alnuaimi, Osama Josephine, Raad Almutairi, Abdullah Dhaifallah Data Brief Data Article Magnetic resonance imaging (MRI) provides a significant key to diagnose and monitor the progression of multiple sclerosis (MS) disease. Manual MS-lesion segmentation, expanded disability status scale (EDSS) and patient's meta information can provide a gold standard for research in terms of automated MS-lesion quantification, automated EDSS prediction and identification of the correlation between MS-lesion and patient disability. In this dataset, we provide a novel multi-sequence MRI dataset of 60 MS patients with consensus manual lesion segmentation, EDSS, general patient information and clinical information. On this dataset, three radiologists and neurologist experts segmented and validated the manual MS-lesion segmentation for three MRI sequences T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR). The dataset can be used to study the relationship between MS-lesion, EDSS and patient clinical information. Furthermore, it also can be used for the development of automated MS-lesion segmentation, patient disability prediction using MRI and correlation analysis between patient disability and MRI brain abnormalities include MS lesion location, size, number and type. Elsevier 2022-04-07 /pmc/articles/PMC9043670/ /pubmed/35496484 http://dx.doi.org/10.1016/j.dib.2022.108139 Text en © 2022 The Author(s). Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Muslim, Ali M.
Mashohor, Syamsiah
Gawwam, Gheyath Al
Mahmud, Rozi
Hanafi, Marsyita binti
Alnuaimi, Osama
Josephine, Raad
Almutairi, Abdullah Dhaifallah
Brain MRI dataset of multiple sclerosis with consensus manual lesion segmentation and patient meta information
title Brain MRI dataset of multiple sclerosis with consensus manual lesion segmentation and patient meta information
title_full Brain MRI dataset of multiple sclerosis with consensus manual lesion segmentation and patient meta information
title_fullStr Brain MRI dataset of multiple sclerosis with consensus manual lesion segmentation and patient meta information
title_full_unstemmed Brain MRI dataset of multiple sclerosis with consensus manual lesion segmentation and patient meta information
title_short Brain MRI dataset of multiple sclerosis with consensus manual lesion segmentation and patient meta information
title_sort brain mri dataset of multiple sclerosis with consensus manual lesion segmentation and patient meta information
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9043670/
https://www.ncbi.nlm.nih.gov/pubmed/35496484
http://dx.doi.org/10.1016/j.dib.2022.108139
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