Longitudinal multiple sclerosis lesion segmentation data resource

Aaron Carass; Snehashis Roy; Amod Jog; Jennifer L. Cuzzocreo; Elizabeth Magrath; Adrian Gherman; Julia Button; James Nguyen; Pierre Louis Bazin; Peter A. Calabresi; Ciprian M. Crainiceanu; Lotta M. Ellingsen; Daniel Salo Reich; Jerry L. Prince; Dzung Pham

doi:10.1016/j.dib.2017.04.004

Longitudinal multiple sclerosis lesion segmentation data resource

Aaron Carass, Snehashis Roy, Amod Jog, Jennifer L. Cuzzocreo, Elizabeth Magrath, Adrian Gherman, Julia Button, James Nguyen, Pierre Louis Bazin, Peter A. Calabresi, Ciprian M. Crainiceanu, Lotta M. Ellingsen, Daniel Salo Reich, Jerry L. Prince, Dzung Pham

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

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.

Original language	English (US)
Pages (from-to)	346-350
Number of pages	5
Journal	Data in Brief
Volume	12
DOIs	https://doi.org/10.1016/j.dib.2017.04.004
State	Published - Jun 1 2017

Keywords

Magnetic resonance imaging
Multiple sclerosis

ASJC Scopus subject areas

General

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10.1016/j.dib.2017.04.004

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@article{c82e7f70670947a7b4500b566c396001,

title = "Longitudinal multiple sclerosis lesion segmentation data resource",

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.",

keywords = "Magnetic resonance imaging, Multiple sclerosis",

author = "Aaron Carass and Snehashis Roy and Amod Jog and Cuzzocreo, {Jennifer L.} and Elizabeth Magrath and Adrian Gherman and Julia Button and James Nguyen and Bazin, {Pierre Louis} and Calabresi, {Peter A.} and Crainiceanu, {Ciprian M.} and Ellingsen, {Lotta M.} and Reich, {Daniel Salo} and Prince, {Jerry L.} and Dzung Pham",

note = "Funding Information: This work was supported in part by the NIH/NINDS Grant R01-NS070906, by the Intramural Research Program of NINDS, and by the National MS Society Grant RG-1507-05243. Prizes for the challenge were furnished by the National MS Society. Publisher Copyright: {\textcopyright} 2017 The Authors",

year = "2017",

month = jun,

day = "1",

doi = "10.1016/j.dib.2017.04.004",

language = "English (US)",

volume = "12",

pages = "346--350",

journal = "Data in Brief",

issn = "2352-3409",

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TY - JOUR

T1 - Longitudinal multiple sclerosis lesion segmentation data resource

AU - Carass, Aaron

AU - Roy, Snehashis

AU - Jog, Amod

AU - Cuzzocreo, Jennifer L.

AU - Magrath, Elizabeth

AU - Gherman, Adrian

AU - Button, Julia

AU - Nguyen, James

AU - Bazin, Pierre Louis

AU - Calabresi, Peter A.

AU - Crainiceanu, Ciprian M.

AU - Ellingsen, Lotta M.

AU - Reich, Daniel Salo

AU - Prince, Jerry L.

AU - Pham, Dzung

N1 - Funding Information: This work was supported in part by the NIH/NINDS Grant R01-NS070906, by the Intramural Research Program of NINDS, and by the National MS Society Grant RG-1507-05243. Prizes for the challenge were furnished by the National MS Society. Publisher Copyright: © 2017 The Authors

PY - 2017/6/1

Y1 - 2017/6/1

N2 - 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.

AB - 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.

KW - Magnetic resonance imaging

KW - Multiple sclerosis

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JO - Data in Brief

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ER -

Longitudinal multiple sclerosis lesion segmentation data resource

Abstract

Keywords

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