title: Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge.
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.
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, Netherlands.
authors.forename: Ivana
authors.initials: I
authors.lastname: Išgum
authors.affiliation: Department of Neonatology, University Medical Center Utrecht, Netherlands.
authors.forename: Manon J N L
authors.initials: MJ
authors.lastname: Benders
authors.affiliation: Penn Image Computing and Science Lab, University of Pennsylvania, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: Avants
authors.affiliation: Centre for Medical Image Computing, University College London, United Kingdom.
authors.forename: M Jorge
authors.initials: MJ
authors.lastname: Cardoso
authors.affiliation: Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom.
authors.forename: Serena J
authors.initials: SJ
authors.lastname: Counsell
authors.affiliation: Division of Development and Growth, Department of Pediatrics, University of Geneva, Switzerland; Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland.
authors.forename: Elda Fischi
authors.initials: EF
authors.lastname: Gomez
authors.affiliation: Division of Development and Growth, Department of Pediatrics, University of Geneva, Switzerland.
authors.forename: Laura
authors.initials: L
authors.lastname: Gui
authors.affiliation: Division of Development and Growth, Department of Pediatrics, University of Geneva, Switzerland.
authors.forename: Petra S
authors.initials: PS
authors.lastname: Hűppi
authors.affiliation: Department of Neonatology, University Medical Center Utrecht, Netherlands.
authors.forename: Karina J
authors.initials: KJ
authors.lastname: Kersbergen
authors.affiliation: Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom; Biomedical Image Analysis Group, Imperial College London, United Kingdom.
authors.forename: Antonios
authors.initials: A
authors.lastname: Makropoulos
authors.affiliation: Centre for Medical Image Computing, University College London, United Kingdom.
authors.forename: Andrew
authors.initials: A
authors.lastname: Melbourne
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, Netherlands.
authors.forename: Pim
authors.initials: P
authors.lastname: Moeskops
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, Netherlands.
authors.forename: Christian P
authors.initials: CP
authors.lastname: Mol
authors.affiliation: Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom.
authors.forename: Maria
authors.initials: M
authors.lastname: Kuklisova-Murgasova
authors.affiliation: Biomedical Image Analysis Group, Imperial College London, United Kingdom.
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, United Kingdom.
authors.forename: Julia A
authors.initials: JA
authors.lastname: Schnabel
authors.affiliation: Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia.
authors.forename: Vedran
authors.initials: V
authors.lastname: Srhoj-Egekher
authors.affiliation: Penn Image Computing and Science Lab, University of Pennsylvania, USA.
authors.forename: Jue
authors.initials: J
authors.lastname: Wu
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, United Kingdom.
authors.forename: Siying
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Neonatology, University Medical Center Utrecht, Netherlands.
authors.forename: Linda S
authors.initials: LS
authors.lastname: de Vries
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, Netherlands.
authors.forename: Max A
authors.initials: MA
authors.lastname: Viergever
mesh_terms: D000465:Algorithms; D001331:Automation; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007231:Infant, Newborn; D007234:Infant, Premature; D008279:Magnetic Resonance Imaging / Q000379:methods*
keywords: Brain segmentation; MRI; Neonatal brain; Segmentation comparison; Segmentation evaluation