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

Autor:	Muslim AM; Department of Computer Science, Dijlah University College, Baghdad, Iraq.; Department of Computer and Communication System Engineering, Universiti Putra Malaysia, Serdang, Malaysia., Mashohor S; Department of Computer and Communication System Engineering, Universiti Putra Malaysia, Serdang, Malaysia., Gawwam GA; Department of Neurology, Baghdad University, Baghdad, Iraq., Mahmud R; Department of Imaging, Universiti Putra Malaysia, Serdang, Malaysia., Hanafi MB; Department of Computer and Communication System Engineering, Universiti Putra Malaysia, Serdang, Malaysia., Alnuaimi O; Department of Radiology and Medical Imaging, Elias Emergency University Hospital, Bucharest, Romania., Josephine R; Department of Radiology and Medical Imaging, Elias Emergency University Hospital, Bucharest, Romania., Almutairi AD; Department of Imaging, Universiti Putra Malaysia, Serdang, Malaysia.
Jazyk:	angličtina
Zdroj:	Data in brief [Data Brief] 2022 Apr 07; Vol. 42, pp. 108139. Date of Electronic Publication: 2022 Apr 07 (Print Publication: 2022).
DOI:	10.1016/j.dib.2022.108139
Abstrakt:	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. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2022 The Author(s). Published by Elsevier Inc.)
Databáze:	MEDLINE
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