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Data Brief. 2017 Apr 8;12:346-350. doi: 10.1016/j.dib.2017.04.004. eCollection 2017 Jun.

Longitudinal multiple sclerosis lesion segmentation data resource.

Author information

1
Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
2
Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
3
CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA.
4
Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
5
Department of Biostatistics, The Johns Hopkins University, Baltimore, MD 21205, USA.
6
Department of Neurophysics, Max Planck Institute, 04103 Leipzig, Germany.
7
Department of Electrical and Computer Engineering, University of Iceland, 107 Reykjavík, Iceland.
8
Translational Neuroradiology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20892, USA.

Abstract

The data presented in this article is related to the research article entitled "Longitudinal multiple sclerosis lesion segmentation: Resource and challenge" (Carass et al., 2017) [1]. In conjunction with the 2015 International Symposium on Biomedical Imaging, we organized a longitudinal multiple sclerosis (MS) lesion segmentation challenge providing training and test data to registered participants. The training data consists of five subjects with a mean of 4.4 (±0.55) time-points, and test data of fourteen subjects with a mean of 4.4 (±0.67) time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. The training data including multi-modal scans and manually delineated lesion masks is available for download. In addition, the testing data is also being made available in conjunction with a website for evaluating the automated analysis of the testing data.

KEYWORDS:

Magnetic resonance imaging; Multiple sclerosis

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