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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
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Elsevier
2022
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| _version_ | 1825938394900856832 |
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| author | Muslim, Ali M. Mashohor, Syamsiah Al Gawwam, Gheyath Mahmud, Rozi Hanafi, Marsyita Alnuaimi, Osama Josephine, Raad Almutairi, Abdullah Dhaifallah |
| author_facet | Muslim, Ali M. Mashohor, Syamsiah Al Gawwam, Gheyath Mahmud, Rozi Hanafi, Marsyita Alnuaimi, Osama Josephine, Raad Almutairi, Abdullah Dhaifallah |
| author_sort | Muslim, Ali M. |
| collection | UPM |
| 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. |
| first_indexed | 2024-03-06T11:13:12Z |
| format | Article |
| id | upm.eprints-100567 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T11:13:12Z |
| publishDate | 2022 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-1005672023-10-10T02:16:48Z http://psasir.upm.edu.my/id/eprint/100567/ Brain MRI dataset of multiple sclerosis with consensus manual lesion segmentation and patient meta information Muslim, Ali M. Mashohor, Syamsiah Al Gawwam, Gheyath Mahmud, Rozi Hanafi, Marsyita Alnuaimi, Osama Josephine, Raad Almutairi, Abdullah Dhaifallah 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 2022-06 Article PeerReviewed Muslim, Ali M. and Mashohor, Syamsiah and Al Gawwam, Gheyath and Mahmud, Rozi and Hanafi, Marsyita and Alnuaimi, Osama and Josephine, Raad and Almutairi, Abdullah Dhaifallah (2022) Brain MRI dataset of multiple sclerosis with consensus manual lesion segmentation and patient meta information. Data in Brief, 42. art. no. 108139. pp. 1-5. ISSN 2352-3409 https://www.sciencedirect.com/science/article/pii/S235234092200347X 10.1016/j.dib.2022.108139 |
| spellingShingle | Muslim, Ali M. Mashohor, Syamsiah Al Gawwam, Gheyath Mahmud, Rozi Hanafi, Marsyita 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 |
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