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  Longitudinal multiple sclerosis lesion segmentation data resource

Carass, A., Roy, S., Jog, A., Cuzzocreo, J. L., Magrath, E., Gherman, A., et al. (2017). Longitudinal multiple sclerosis lesion segmentation data resource. Data in Brief, 12, 346-350. doi:10.1016/j.dib.2017.04.004.

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Genre: Journal Article

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 Creators:
Carass, Aaron1, 2, Author
Roy, Snehashis3, Author
Jog, Amod2, Author
Cuzzocreo, Jennifer L.4, Author
Magrath, Elizabeth3, Author
Gherman, Adrian5, Author
Button, Julia4, Author
Nguyen, James4, Author
Bazin, Pierre-Louis6, Author           
Calabresi, Peter A.4, Author
Crainiceanu, Ciprian M.5, Author
Ellingsen, Lotta M.1, 7, Author
Reich, Daniel S.4, 8, Author
Prince, Jerry L.1, 2, Author
Pham, Dzung L.3, Author
Affiliations:
1Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA, ou_persistent22              
2Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA, ou_persistent22              
3Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation for Advancement of Military Medicine, Bethesda, MD, USA, ou_persistent22              
4Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA, ou_persistent22              
5Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA, ou_persistent22              
6Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634550              
7Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland, ou_persistent22              
8Translational Neuroradiology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA, ou_persistent22              

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Free keywords: Magnetic resonance imaging; Multiple sclerosis
 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.1 In addition, the testing data is also being made available in conjunction with a website for evaluating the automated analysis of the testing data.

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Language(s): eng - English
 Dates: 2017-02-012017-01-062017-04-042017-04-08
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.dib.2017.04.004
PMID: 28491937
 Degree: -

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Project name : -
Grant ID : R01-NS070906
Funding program : -
Funding organization : National Institutes of Health (NIH)
Project name : -
Grant ID : -
Funding program : Intramural Research Program
Funding organization : National Institute of Neurological Disorders and Stroke (NINDS)
Project name : -
Grant ID : RG-1507-05243
Funding program : National MS Society Grant
Funding organization : National MS Society

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Title: Data in Brief
  Abbreviation : Data Brief
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Dordrecht : Elsevier
Pages: - Volume / Issue: 12 Sequence Number: - Start / End Page: 346 - 350 Identifier: ISSN: 2352-3409
CoNE: https://pure.mpg.de/cone/journals/resource/2352-3409