TY - GEN
T1 - Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images
AU - Moeskops, Pim
AU - Viergever, Max A.
AU - Benders, Manon J.N.L.
AU - Išgum, Ivana
N1 - Publisher Copyright: © 2015 SPIE.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Automatic brain segmentation
KW - MRI
KW - Supervised voxel classification
UR - http://www.scopus.com/inward/record.url?scp=84943396572&partnerID=8YFLogxK
U2 - https://doi.org/10.1117/12.2081833
DO - https://doi.org/10.1117/12.2081833
M3 - Conference contribution
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2015
A2 - Styner, Martin A.
A2 - Ourselin, Sebastien
PB - SPIE
T2 - Medical Imaging 2015: Image Processing
Y2 - 24 February 2015 through 26 February 2015
ER -