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Neuroimage. 2017 Jan 11;148:77-102. doi: 10.1016/j.neuroimage.2016.12.064. [Epub ahead of print]

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.

Author information

  • 1Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA. Electronic address: aaron_carass@jhu.edu.
  • 2CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA.
  • 3Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
  • 4Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
  • 5Department of Biostatistics, The Johns Hopkins University, Baltimore, MD 21205, USA.
  • 6Translational Imaging Group, CMIC, UCL, NW1 2HE London, UK; NMR Research Unit, UCL Institute of Neurology, WC1N 3BG London, UK.
  • 7Translational Imaging Group, CMIC, UCL, NW1 2HE London, UK.
  • 8Translational Imaging Group, CMIC, UCL, NW1 2HE London, UK; Dementia Research Centre, UCL Institute of Neurology, WC1N 3BG London, UK.
  • 9NMR Research Unit, UCL Institute of Neurology, WC1N 3BG London, UK.
  • 10VisAGeS: INSERM U746, CNRS UMR6074, INRIA, University of Rennes I, France.
  • 11Computational Radiology Laboratory, Boston Childrens Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA.
  • 12Biomedical Imaging Lab, Department of Engineering Design, Indian Institute of Technology, Chennai 600036, India.
  • 13Centre For Intelligent Machines, McGill University, Montréal, QC H3A 0E9, Canada.
  • 14Institute of Medical Informatics, University of Lübeck, 23538 Lübeck, Germany.
  • 15Bahçeşehir University, Faculty of Engineering and Natural Sciences, 34349 Beşiktaş, Turkey.
  • 16icometrix, 3012 Leuven, Belgium.
  • 17Institute for Computing and Information Sciences, Radboud University, 6525 HP Nijmegen, Netherlands.
  • 18Diagnostic Image Analysis Group, Radboud University Medical Center, 6525 GA Nijmegen, Netherlands.
  • 19Department of Electrical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel.
  • 20Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel.
  • 21Department of Neurophysics, Max Planck Institute, 04103 Leipzig, Germany.
  • 22Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Electrical and Computer Engineering, University of Iceland, 107 Reykjavík, Iceland.
  • 23Department of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA; Translational Neuroradiology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20892, USA.
  • 24Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.

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 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.

KEYWORDS:

Magnetic resonance imaging; Multiple sclerosis

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