Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

P. Moeskops, M.A. Viergever, M.J.N.L. Benders, I. Išgum

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

11 Citations (Scopus)
1 Downloads (Pure)


Automatic brain tissue segmentation is of clinical relevance in images acquired at all ages. The literature presents a clear distinction between methods developed for MR images of infants, and methods developed for images of adults. The aim of this work is to evaluate a method developed for neonatal images in the segmentation of adult images. The evaluated method employs supervised voxel classification in subsequent stages, exploiting spatial and intensity information. Evaluation was performed using images available within the MRBrainS13 challenge. The obtained average Dice coefficients were 85.77% for grey matter, 88.66% for white matter, 81.08% for cerebrospinal fluid, 95.65% for cerebrum, and 96.92% for intracranial cavity, currently resulting in the best overall ranking. The possibility of applying the same method to neonatal as well as adult images can be of great value in cross-sectional studies that include a wide age range. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Original languageEnglish
Title of host publicationMedical Imaging 2015 : Image Processing. 24-26 February 2015, Orlando, Florida, United States
EditorsS. Ourselin, M.A. Styner
Place of PublicationBellingham
Number of pages6
ISBN (Electronic)9781628415032
Publication statusPublished - 2015
Externally publishedYes
EventSPIE Medical Imaging 2015 - SeaWorld, Orlando, United States
Duration: 21 Feb 201526 Feb 2015

Publication series

NameProceedings of SPIE
NumberPart 1
ISSN (Electronic)1605-7422
NameProgress in Biomedical Optics and Imaging
ISSN (Electronic)1605-7422


ConferenceSPIE Medical Imaging 2015
Abbreviated titleSPIE 2015
Country/TerritoryUnited States
Internet address


  • Automatic brain segmentation
  • Supervised voxel classification
  • MRI


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