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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge.
Med. Image Anal. 20, 135-151 (2015)
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
ISSN (print) / ISBN
1361-8415
e-ISSN
1361-8415
Zeitschrift
Medical Image Analysis
Quellenangaben
Band: 20,
Heft: 1,
Seiten: 135-151
Verlag
Elsevier
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute for Machine Learning in Biomed Imaging (IML)