Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge

Isgum I, Benders MJNL, Avants B, Cardoso MJ, Counsell SJ, Gomez EF, Gui L, Huppi PS, Kersbergen KJ, Makropoulos A, Melbourne A, Moeskops P, Mol CP, Kuklisova-Murgasova M, Rueckert D, Schnabel JA, Srhoj-Egekher V, Wu J, Wang S, De Vries LS, Viergever MA (2015)

Publication Type: Journal article

Publication year: 2015

Journal

Medical Image Analysis Elsevier

Book Volume: 20

Pages Range: 135-151

Journal Issue: 1

DOI: 10.1016/j.media.2014.11.001

Abstract

A number of algorithms for brain segmentation in preterm born infants have been published, but a reliable comparison of their performance is lacking. The NeoBrainS12 study (http://neobrains12.isi.uu.nl), providing three different image sets of preterm born infants, was set up to provide such a comparison. These sets are (i) axial scans acquired at 40. weeks corrected age, (ii) coronal scans acquired at 30. weeks corrected age and (iii) coronal scans acquired at 40. weeks corrected age. Each of these three sets consists of three T1- and T2-weighted MR images of the brain acquired with a 3T MRI scanner. The task was to segment cortical grey matter, non-myelinated and myelinated white matter, brainstem, basal ganglia and thalami, cerebellum, and cerebrospinal fluid in the ventricles and in the extracerebral space separately. Any team could upload the results and all segmentations were evaluated in the same way. This paper presents the results of eight participating teams. The results demonstrate that the participating methods were able to segment all tissue classes well, except myelinated white matter.

Involved external institutions

University Medical Centre Utrecht (UMC Utrecht) NL Netherlands (NL) University of Pennsylvania US United States (USA) (US) University College London (UCL) GB United Kingdom (GB) King’s College London GB United Kingdom (GB) University of Geneva / Université de Genève (UNIGE) CH Switzerland (CH) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) University of Oxford GB United Kingdom (GB) University of Zagreb / Sveučilište u Zagrebu HR Croatia (HR)

How to cite

APA:

Isgum, I., Benders, M.J.N.L., Avants, B., Cardoso, M.J., Counsell, S.J., Gomez, E.F.,... Viergever, M.A. (2015). Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge. Medical Image Analysis, 20(1), 135-151. https://doi.org/10.1016/j.media.2014.11.001

MLA:

Isgum, Ivana, et al. "Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge." Medical Image Analysis 20.1 (2015): 135-151.

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