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Isgum, I.* ; Benders, M.J.N.L.* ; Avants, B.* ; Cardoso, M.J.* ; Counsell, S.J.* ; Gomez, E.F.* ; Gui, L.* ; Huppi, P.S.* ; Kersbergen, K.J.* ; Makropoulos, A.* ; Melbourne, A.* ; Moeskops, P.* ; Mol, C.P.* ; Kuklisova-Murgasova, M.* ; Rueckert, D.* ; Schnabel, J.A.* ; Srhoj-Egekher, V.* ; Wu, J.* ; Wang, S.* ; de Vries, L.S.* ; Viergever, M.A.*

Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge.

Med. Image Anal. 20, 135-151 (2015)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Brain Segmentation ; Mri ; Neonatal Brain ; Segmentation Comparison ; Segmentation Evaluation
Language english
Publication Year 2015
HGF-reported in Year 2015
ISSN (print) / ISBN 1361-8415
e-ISSN 1361-8415
Journal Medical Image Analysis
Quellenangaben Volume: 20, Issue: 1, Pages: 135-151 Article Number: , Supplement: ,
Publisher Elsevier
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-507100-001
DOI 10.1016/j.media.2014.11.001
Scopus ID 84920895738
PubMed ID 25487610
Erfassungsdatum 2022-09-06
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