Longitudinal multiple sclerosis lesion segmentation data resource
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 scl...
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| Published in | Data in brief Vol. 12; no. C; pp. 346 - 350 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier Inc
01.06.2017
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2352-3409 2352-3409 |
| DOI | 10.1016/j.dib.2017.04.004 |
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| 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.11The data and evaluation website is: http://smart-stats-tools.org/lesion-challenge-2015. 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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| AbstractList | 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. 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.¹1The data and evaluation website is: http://smart-stats-tools.org/lesion-challenge-2015. In addition, the testing data is also being made available in conjunction with a website for evaluating the automated analysis of the testing data. 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. 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.11The data and evaluation website is: http://smart-stats-tools.org/lesion-challenge-2015. In addition, the testing data is also being made available in conjunction with a website for evaluating the automated analysis of the testing data. 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.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. |
| Author | Reich, Daniel S. Cuzzocreo, Jennifer L. Carass, Aaron Roy, Snehashis Magrath, Elizabeth Pham, Dzung L. Bazin, Pierre-Louis Jog, Amod Button, Julia Crainiceanu, Ciprian M. Nguyen, James Ellingsen, Lotta M. Prince, Jerry L. Gherman, Adrian Calabresi, Peter A. |
| AuthorAffiliation | a Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA g Department of Electrical and Computer Engineering, University of Iceland, 107 Reykjavík, Iceland h Translational Neuroradiology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20892, USA f Department of Neurophysics, Max Planck Institute, 04103 Leipzig, Germany e Department of Biostatistics, The Johns Hopkins University, Baltimore, MD 21205, USA d Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA b Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA c CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA |
| AuthorAffiliation_xml | – name: b Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA – name: g Department of Electrical and Computer Engineering, University of Iceland, 107 Reykjavík, Iceland – name: e Department of Biostatistics, The Johns Hopkins University, Baltimore, MD 21205, USA – name: d Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – name: a Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA – name: h Translational Neuroradiology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20892, USA – name: c CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA – name: f Department of Neurophysics, Max Planck Institute, 04103 Leipzig, Germany |
| Author_xml | – sequence: 1 givenname: Aaron surname: Carass fullname: Carass, Aaron email: aaron_carass@jhu.edu organization: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA – sequence: 2 givenname: Snehashis surname: Roy fullname: Roy, Snehashis organization: CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA – sequence: 3 givenname: Amod surname: Jog fullname: Jog, Amod organization: Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA – sequence: 4 givenname: Jennifer L. surname: Cuzzocreo fullname: Cuzzocreo, Jennifer L. organization: Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – sequence: 5 givenname: Elizabeth surname: Magrath fullname: Magrath, Elizabeth organization: CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA – sequence: 6 givenname: Adrian surname: Gherman fullname: Gherman, Adrian organization: Department of Biostatistics, The Johns Hopkins University, Baltimore, MD 21205, USA – sequence: 7 givenname: Julia surname: Button fullname: Button, Julia organization: Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – sequence: 8 givenname: James surname: Nguyen fullname: Nguyen, James organization: Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – sequence: 9 givenname: Pierre-Louis surname: Bazin fullname: Bazin, Pierre-Louis organization: Department of Neurophysics, Max Planck Institute, 04103 Leipzig, Germany – sequence: 10 givenname: Peter A. surname: Calabresi fullname: Calabresi, Peter A. organization: Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – sequence: 11 givenname: Ciprian M. surname: Crainiceanu fullname: Crainiceanu, Ciprian M. organization: Department of Biostatistics, The Johns Hopkins University, Baltimore, MD 21205, USA – sequence: 12 givenname: Lotta M. surname: Ellingsen fullname: Ellingsen, Lotta M. organization: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA – sequence: 13 givenname: Daniel S. surname: Reich fullname: Reich, Daniel S. organization: Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – sequence: 14 givenname: Jerry L. surname: Prince fullname: Prince, Jerry L. organization: Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA – sequence: 15 givenname: Dzung L. surname: Pham fullname: Pham, Dzung L. organization: CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA |
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| Cites_doi | 10.1002/hbm.22409 10.1109/ISBI.2007.356937 10.54294/lmkqvm 10.1016/j.neuroimage.2016.12.064 10.1016/j.neuroimage.2011.03.045 10.1109/TMI.2010.2046908 10.1016/S1053-8119(09)70884-5 10.1016/j.neuroimage.2011.02.076 |
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| Keywords | Magnetic resonance imaging Multiple sclerosis |
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| References | A. Carass, M.B. Wheeler, J. Cuzzocreo, P.-L. Bazin, S.S. Bassett, J.L. Prince, A joint registration and segmentation approach to skull stripping, in: Proceedings of the 4th International Symposium on Biomedical Imaging, ISBI 2007, IEEE, 2007, pp. 656–659. Fonov, Evans, McKinstry, Almli, Collins (bib6) 2009; 47 Tustison, Avants, Cook, Zheng, Egan, Yushkevich, Gee (bib2) 2010; 29 Shiee, Bazin, Cuzzocreo, Ye, Kishore, Carass, Calabresi, Reich, Prince, Pham (bib5) 2014; 35 Carass, Cuzzocreo, Wheeler, Bazin, Resnick, Prince. (bib4) 2010; 56 Reuter, Fischl (bib7) 2011; 57 Carass, Roy, Jog, Cuzzocreo, Magrath, Gherman, But- ton, Nguyen, Prados, Sudre, Cawley, Cardoso, Cic- carelli, Wheeler-Kingshott, Catanese, Ourselin, Deshpande, Maurel, Commowick, Barillot, Tomas-Fernandez, Vaidya, Muthuganapathy, Chunduru, Krishnamurthi, Arbel, Handels, Maier, Iheme, Unay, Jain, Sima, Ghafoorian, Smeets, Platel, Birenbaum, Greenspan, Cal- abresi, Bazin, Crainiceanu, Ellingsen, Reich, Prince, Pham (bib1) 2017, 148(c), 2017 M. Styner, J. Lee, B. Chin, M.S. Chin, O. Commowick, H.-H. Tran, S. Markovic-Plese, V. Jewells, S. Warfield, 3D segmentation in the clinic: a grand challenge II: MS lesion segmentation, in: Proceedings of the 11th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2008, 2008, pp. 1–6. 10.1016/j.dib.2017.04.004_bib3 Shiee (10.1016/j.dib.2017.04.004_bib5) 2014; 35 Fonov (10.1016/j.dib.2017.04.004_bib6) 2009; 47 Reuter (10.1016/j.dib.2017.04.004_bib7) 2011; 57 Tustison (10.1016/j.dib.2017.04.004_bib2) 2010; 29 Carass (10.1016/j.dib.2017.04.004_bib1) 2017 Carass (10.1016/j.dib.2017.04.004_bib4) 2010; 56 10.1016/j.dib.2017.04.004_bib8 21376812 - Neuroimage. 2011 Jul 1;57(1):19-21 24382742 - Hum Brain Mapp. 2014 Jul;35(7):3385-401 28087490 - Neuroimage. 2017 Mar 1;148:77-102 21458576 - Neuroimage. 2011 Jun 15;56(4):1982-92 20378467 - IEEE Trans Med Imaging. 2010 Jun;29(6):1310-20 |
| References_xml | – reference: A. Carass, M.B. Wheeler, J. Cuzzocreo, P.-L. Bazin, S.S. Bassett, J.L. Prince, A joint registration and segmentation approach to skull stripping, in: Proceedings of the 4th International Symposium on Biomedical Imaging, ISBI 2007, IEEE, 2007, pp. 656–659. – reference: M. Styner, J. Lee, B. Chin, M.S. Chin, O. Commowick, H.-H. Tran, S. Markovic-Plese, V. Jewells, S. Warfield, 3D segmentation in the clinic: a grand challenge II: MS lesion segmentation, in: Proceedings of the 11th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2008, 2008, pp. 1–6. – start-page: 77 year: 2017, 148(c), 2017 end-page: 102 ident: bib1 article-title: Longitudinal multiple sclerosis lesion segmentation: resource & challenge publication-title: NeuroImage – volume: 35 start-page: 3385 year: 2014 end-page: 3401 ident: bib5 article-title: Reconstruction of the human cerebral cortex robust to white matter lesions: method and validation publication-title: Hum. Brain Mapp. – volume: 57 start-page: 19 year: 2011 end-page: 21 ident: bib7 article-title: Avoiding asymmetry-induced bias in longitudinal. Image processing publication-title: NeuroImage – volume: 29 start-page: 1310 year: 2010 end-page: 1320 ident: bib2 article-title: N4ITK: improved N3 bias correction publication-title: IEEE Trans. Med. Imag. – volume: 56 start-page: 1982 year: 2010 end-page: 1992 ident: bib4 article-title: Simple paradigm for extra-cerebral tissue removal: algorithm and analysis publication-title: NeuroImage – volume: 47 start-page: S102 year: 2009 ident: bib6 article-title: Unbiased nonlinear average age-appropriate brain templates from birth to adulthood publication-title: NeuroImage – volume: 35 start-page: 3385 issue: 7 year: 2014 ident: 10.1016/j.dib.2017.04.004_bib5 article-title: Reconstruction of the human cerebral cortex robust to white matter lesions: method and validation publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.22409 – ident: 10.1016/j.dib.2017.04.004_bib3 doi: 10.1109/ISBI.2007.356937 – ident: 10.1016/j.dib.2017.04.004_bib8 doi: 10.54294/lmkqvm – start-page: 77 year: 2017 ident: 10.1016/j.dib.2017.04.004_bib1 article-title: Longitudinal multiple sclerosis lesion segmentation: resource & challenge publication-title: NeuroImage doi: 10.1016/j.neuroimage.2016.12.064 – volume: 56 start-page: 1982 issue: 4 year: 2010 ident: 10.1016/j.dib.2017.04.004_bib4 article-title: Simple paradigm for extra-cerebral tissue removal: algorithm and analysis publication-title: NeuroImage doi: 10.1016/j.neuroimage.2011.03.045 – volume: 29 start-page: 1310 issue: 6 year: 2010 ident: 10.1016/j.dib.2017.04.004_bib2 article-title: N4ITK: improved N3 bias correction publication-title: IEEE Trans. Med. Imag. doi: 10.1109/TMI.2010.2046908 – volume: 47 start-page: S102 issue: S1 year: 2009 ident: 10.1016/j.dib.2017.04.004_bib6 article-title: Unbiased nonlinear average age-appropriate brain templates from birth to adulthood publication-title: NeuroImage doi: 10.1016/S1053-8119(09)70884-5 – volume: 57 start-page: 19 issue: 1 year: 2011 ident: 10.1016/j.dib.2017.04.004_bib7 article-title: Avoiding asymmetry-induced bias in longitudinal. Image processing publication-title: NeuroImage doi: 10.1016/j.neuroimage.2011.02.076 – reference: 20378467 - IEEE Trans Med Imaging. 2010 Jun;29(6):1310-20 – reference: 21376812 - Neuroimage. 2011 Jul 1;57(1):19-21 – reference: 21458576 - Neuroimage. 2011 Jun 15;56(4):1982-92 – reference: 24382742 - Hum Brain Mapp. 2014 Jul;35(7):3385-401 – reference: 28087490 - Neuroimage. 2017 Mar 1;148:77-102 |
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| Title | Longitudinal multiple sclerosis lesion segmentation data resource |
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