Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge

• Published in: Medical image analysis Vol. 20; no. 1; pp. 135 - 151
• Main Authors: Išgum, Ivana, Benders, Manon J.N.L., Avants, Brian, Cardoso, M. Jorge, Counsell, Serena J., Gomez, Elda Fischi, Gui, Laura, Hűppi, Petra S., Kersbergen, Karina J., Makropoulos, Antonios, Melbourne, Andrew, Moeskops, Pim, Mol, Christian P., Kuklisova-Murgasova, Maria, Rueckert, Daniel, Schnabel, Julia A., Srhoj-Egekher, Vedran, Wu, Jue, Wang, Siying, de Vries, Linda S., Viergever, Max A.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.02.2015
• Subjects: Algorithms; Automation; Brain - anatomy & histology; Brain segmentation; Humans; Image Interpretation, Computer-Assisted; Infant, Newborn; Infant, Premature; Magnetic Resonance Imaging - methods; MRI; Neonatal brain; Segmentation comparison; Segmentation evaluation; Segmentation evaluation; Brain segmentation; Neonatal brain; MRI; Segmentation comparison
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2014.11.001

[Display omitted] •Automatic algorithms for neonatal brain segmentation in MR images were compared.•Preterm infants underwent MRI at 30 and 40 weeks corrected gestational age.•Images were acquired axially and coronally with a 3T MRI scanner.•Automatic segmentation of brain tissues in neonatal brain MRI is feasible.•Automatic segmentation of myelinated white matter in these images is not reliable. 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 40weeks corrected age, (ii) coronal scans acquired at 30weeks corrected age and (iii) coronal scans acquired at 40weeks 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.