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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 möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Brain Segmentation ; Mri ; Neonatal Brain ; Segmentation Comparison ; Segmentation Evaluation
Sprache englisch
Veröffentlichungsjahr 2015
HGF-Berichtsjahr 2015
ISSN (print) / ISBN 1361-8415
e-ISSN 1361-8415
Zeitschrift Medical Image Analysis
Quellenangaben Band: 20, Heft: 1, Seiten: 135-151 Artikelnummer: , Supplement: ,
Verlag Elsevier
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) 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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