Longitudinal multiple sclerosis lesion segmentation: Resource and challenge
In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 148; pp. 77 - 102 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.03.2017
Elsevier Limited Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1053-8119 1095-9572 1095-9572 |
| DOI | 10.1016/j.neuroimage.2016.12.064 |
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| Abstract | In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website22The Challenge Evaluation Website is: http://smart-stats-tools.org/lesion-challenge-2015 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.
•Public lesion data base of 21 training data sets and 61 testing data sets.•Fully automated evaluation website.•Comparison between 14 state-of-the-art algorithms and 2 manual delineators. |
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| AbstractList | In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website22The Challenge Evaluation Website is: http://smart-stats-tools.org/lesion-challenge-2015 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.
•Public lesion data base of 21 training data sets and 61 testing data sets.•Fully automated evaluation website.•Comparison between 14 state-of-the-art algorithms and 2 manual delineators. In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters. In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website22The Challenge Evaluation Website is:http://smart-stats-tools.org/lesion-challenge-2015as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters. In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website 2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters. In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: 1) the sharing of a rich data set; 2) collaboration and comparison of the various avenues of research being pursued in the community; and 3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website1 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters. In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters. |
| Author | Ciccarelli, Olga Birenbaum, Ariel Platel, Bram Sudre, Carole H. Cuzzocreo, Jennifer L. Catanese, Laurence Jain, Saurabh Iheme, Leonardo O. Roy, Snehashis Magrath, Elizabeth Pham, Dzung L. Bazin, Pierre-Louis Deshpande, Hrishikesh Maurel, Pierre Muthuganapathy, Ramanathan Nguyen, James Cawley, Niamh Vaidya, Suthirth Warfield, Simon K. Arbel, Tal Unay, Devrim Jesson, Andrew Ghafoorian, Mohsen Reich, Daniel S. Handels, Heinz Greenspan, Hayit Ourselin, Sébastien Krishnamurthi, Ganapathy Carass, Aaron Wheeler-Kingshott, Claudia A.M. Chunduru, Abhijith Sima, Diana M. Barillot, Christian Jog, Amod Commowick, Olivier Smeets, Dirk Button, Julia Crainiceanu, Ciprian M. Jorge Cardoso, Manuel Tomas-Fernandez, Xavier Maier, Oskar Ellingsen, Lotta M. Prince, Jerry L. Gherman, Adrian Prados, Ferran Calabresi, Peter A. |
| AuthorAffiliation | a Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA j Computational Radiology Laboratory, Boston Childrens Hospital, Boston, MA 02115, USA o Bahçeşehir University, Faculty of Engineering and Natural Sciences, 34349 Beşiktaş, Turkey i VisAGeS: INSERM U746, CNRS UMR6074, INRIA, University of Rennes I, France e Department of Biostatistics, The Johns Hopkins University, Baltimore, MD 21205, USA f Translational Imaging Group, CMIC, UCL, NW1 2HE London, UK d Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA v Department of Electrical and Computer Engineering, University of Iceland, 107 Reykjavík, Iceland c CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA m Centre For Intelligent Machines, McGill University, Montréal, QC H3A 0E9, Canada q Institute for Computing and Information Sciences, Radboud University, 6525 HP Nijmegen, Netherlands u Department of Neur |
| AuthorAffiliation_xml | – name: e Department of Biostatistics, The Johns Hopkins University, Baltimore, MD 21205, USA – name: n Institute of Medical Informatics, University of Lübeck, 23538 Lübeck, Germany – name: u Department of Neurophysics, Max Planck Institute, 04103 Leipzig, Germany – name: h Dementia Research Centre, UCL Institute of Neurology, WC1N 3BG London, UK – name: a Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA – name: t Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel – name: i VisAGeS: INSERM U746, CNRS UMR6074, INRIA, University of Rennes I, France – name: r Diagnostic Image Analysis Group, Radboud University Medical Center, 6525 GA Nijmegen, Netherlands – name: f Translational Imaging Group, CMIC, UCL, NW1 2HE London, UK – name: l Biomedical Imaging Lab, Department of Engineering Design, Indian Institute of Technology, Chennai 600036, India – name: s Department of Electrical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel – name: v Department of Electrical and Computer Engineering, University of Iceland, 107 Reykjavík, Iceland – name: m Centre For Intelligent Machines, McGill University, Montréal, QC H3A 0E9, Canada – name: b Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA – name: p ico metrix , 3012 Leuven, Belgium – name: d Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – name: k Harvard Medical School, Boston, MA 02115, USA – name: q Institute for Computing and Information Sciences, Radboud University, 6525 HP Nijmegen, Netherlands – name: g NMR Research Unit, UCL Institute of Neurology, WC1N 3BG London, UK – name: w Translational Neuroradiology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20892, USA – name: o Bahçeşehir University, Faculty of Engineering and Natural Sciences, 34349 Beşiktaş, Turkey – name: c CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA – name: j Computational Radiology Laboratory, Boston Childrens Hospital, Boston, MA 02115, USA |
| 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: Ferran surname: Prados fullname: Prados, Ferran organization: Translational Imaging Group, CMIC, UCL, NW1 2HE London, UK – sequence: 10 givenname: Carole H. surname: Sudre fullname: Sudre, Carole H. organization: Translational Imaging Group, CMIC, UCL, NW1 2HE London, UK – sequence: 11 givenname: Manuel surname: Jorge Cardoso fullname: Jorge Cardoso, Manuel organization: Translational Imaging Group, CMIC, UCL, NW1 2HE London, UK – sequence: 12 givenname: Niamh surname: Cawley fullname: Cawley, Niamh organization: NMR Research Unit, UCL Institute of Neurology, WC1N 3BG London, UK – sequence: 13 givenname: Olga surname: Ciccarelli fullname: Ciccarelli, Olga organization: NMR Research Unit, UCL Institute of Neurology, WC1N 3BG London, UK – sequence: 14 givenname: Claudia A.M. surname: Wheeler-Kingshott fullname: Wheeler-Kingshott, Claudia A.M. organization: NMR Research Unit, UCL Institute of Neurology, WC1N 3BG London, UK – sequence: 15 givenname: Sébastien surname: Ourselin fullname: Ourselin, Sébastien organization: Translational Imaging Group, CMIC, UCL, NW1 2HE London, UK – sequence: 16 givenname: Laurence surname: Catanese fullname: Catanese, Laurence organization: VisAGeS: INSERM U746, CNRS UMR6074, INRIA, University of Rennes I, France – sequence: 17 givenname: Hrishikesh surname: Deshpande fullname: Deshpande, Hrishikesh organization: VisAGeS: INSERM U746, CNRS UMR6074, INRIA, University of Rennes I, France – sequence: 18 givenname: Pierre surname: Maurel fullname: Maurel, Pierre organization: VisAGeS: INSERM U746, CNRS UMR6074, INRIA, University of Rennes I, France – sequence: 19 givenname: Olivier surname: Commowick fullname: Commowick, Olivier organization: VisAGeS: INSERM U746, CNRS UMR6074, INRIA, University of Rennes I, France – sequence: 20 givenname: Christian surname: Barillot fullname: Barillot, Christian organization: VisAGeS: INSERM U746, CNRS UMR6074, INRIA, University of Rennes I, France – sequence: 21 givenname: Xavier surname: Tomas-Fernandez fullname: Tomas-Fernandez, Xavier organization: Computational Radiology Laboratory, Boston Childrens Hospital, Boston, MA 02115, USA – sequence: 22 givenname: Simon K. surname: Warfield fullname: Warfield, Simon K. organization: Computational Radiology Laboratory, Boston Childrens Hospital, Boston, MA 02115, USA – sequence: 23 givenname: Suthirth surname: Vaidya fullname: Vaidya, Suthirth organization: Biomedical Imaging Lab, Department of Engineering Design, Indian Institute of Technology, Chennai 600036, India – sequence: 24 givenname: Abhijith surname: Chunduru fullname: Chunduru, Abhijith organization: Biomedical Imaging Lab, Department of Engineering Design, Indian Institute of Technology, Chennai 600036, India – sequence: 25 givenname: Ramanathan surname: Muthuganapathy fullname: Muthuganapathy, Ramanathan organization: Biomedical Imaging Lab, Department of Engineering Design, Indian Institute of Technology, Chennai 600036, India – sequence: 26 givenname: Ganapathy surname: Krishnamurthi fullname: Krishnamurthi, Ganapathy organization: Biomedical Imaging Lab, Department of Engineering Design, Indian Institute of Technology, Chennai 600036, India – sequence: 27 givenname: Andrew surname: Jesson fullname: Jesson, Andrew organization: Centre For Intelligent Machines, McGill University, Montréal, QC H3A 0E9, Canada – sequence: 28 givenname: Tal surname: Arbel fullname: Arbel, Tal organization: Centre For Intelligent Machines, McGill University, Montréal, QC H3A 0E9, Canada – sequence: 29 givenname: Oskar surname: Maier fullname: Maier, Oskar organization: Institute of Medical Informatics, University of Lübeck, 23538 Lübeck, Germany – sequence: 30 givenname: Heinz surname: Handels fullname: Handels, Heinz organization: Institute of Medical Informatics, University of Lübeck, 23538 Lübeck, Germany – sequence: 31 givenname: Leonardo O. surname: Iheme fullname: Iheme, Leonardo O. organization: Bahçeşehir University, Faculty of Engineering and Natural Sciences, 34349 Beşiktaş, Turkey – sequence: 32 givenname: Devrim surname: Unay fullname: Unay, Devrim organization: Bahçeşehir University, Faculty of Engineering and Natural Sciences, 34349 Beşiktaş, Turkey – sequence: 33 givenname: Saurabh surname: Jain fullname: Jain, Saurabh organization: icometrix, 3012 Leuven, Belgium – sequence: 34 givenname: Diana M. surname: Sima fullname: Sima, Diana M. organization: icometrix, 3012 Leuven, Belgium – sequence: 35 givenname: Dirk surname: Smeets fullname: Smeets, Dirk organization: icometrix, 3012 Leuven, Belgium – sequence: 36 givenname: Mohsen surname: Ghafoorian fullname: Ghafoorian, Mohsen organization: Institute for Computing and Information Sciences, Radboud University, 6525 HP Nijmegen, Netherlands – sequence: 37 givenname: Bram surname: Platel fullname: Platel, Bram organization: Diagnostic Image Analysis Group, Radboud University Medical Center, 6525 GA Nijmegen, Netherlands – sequence: 38 givenname: Ariel surname: Birenbaum fullname: Birenbaum, Ariel organization: Department of Electrical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel – sequence: 39 givenname: Hayit surname: Greenspan fullname: Greenspan, Hayit organization: Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel – sequence: 40 givenname: Pierre-Louis surname: Bazin fullname: Bazin, Pierre-Louis organization: Department of Neurophysics, Max Planck Institute, 04103 Leipzig, Germany – sequence: 41 givenname: Peter A. surname: Calabresi fullname: Calabresi, Peter A. organization: Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – sequence: 42 givenname: Ciprian M. surname: Crainiceanu fullname: Crainiceanu, Ciprian M. organization: Department of Biostatistics, The Johns Hopkins University, Baltimore, MD 21205, USA – sequence: 43 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: 44 givenname: Daniel S. surname: Reich fullname: Reich, Daniel S. organization: Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA – sequence: 45 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: 46 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 |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28087490$$D View this record in MEDLINE/PubMed https://inserm.hal.science/inserm-01480156$$DView record in HAL |
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| SubjectTerms | Adult Algorithms Automation Bioengineering Female Humans Image Processing, Computer-Assisted Imaging, Three-Dimensional Life Sciences Longitudinal Studies Magnetic Resonance Imaging Male Middle Aged Multiple sclerosis Multiple Sclerosis - diagnostic imaging Neural networks Neurons and Cognition NMR Nuclear magnetic resonance Observer Variation White Matter - diagnostic imaging |
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| Title | Longitudinal multiple sclerosis lesion segmentation: Resource and challenge |
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