Magnetic Resonance Imaging of the Newborn Brain: Automatic Segmentation of Brain Images into 50 Anatomical Regions
Open Access
- 2 April 2013
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 8 (4), e59990
- https://doi.org/10.1371/journal.pone.0059990
Abstract
We studied methods for the automatic segmentation of neonatal and developing brain images into 50 anatomical regions, utilizing a new set of manually segmented magnetic resonance (MR) images from 5 term-born and 15 preterm infants imaged at term corrected age called ALBERTs. Two methods were compared: individual registrations with label propagation and fusion; and template based registration with propagation of a maximum probability neonatal ALBERT (MPNA). In both cases we evaluated the performance of different neonatal atlases and MPNA, and the approaches were compared with the manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across regions, were 0.81±0.02 using label propagation and fusion for the preterm population, and 0.81±0.02 using the single registration of a MPNA for the term population. Segmentations of 36 further unsegmented target images of developing brains yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled age-specific brain atlases for neonates and the developing brain.Keywords
This publication has 77 references indexed in Scilit:
- BEaST: Brain extraction based on nonlocal segmentation techniqueNeuroImage, 2012
- Infant Brain Atlases from Neonates to 1- and 2-Year-OldsPLOS ONE, 2011
- Multi-contrast human neonatal brain atlas: Application to normal neonate development analysisNeuroImage, 2011
- Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detectionNeuroImage, 2010
- Infant brain probability templates for MRI segmentation and normalizationNeuroImage, 2008
- Validation of a fully automated 3D hippocampal segmentation method using subjects with Alzheimer's disease mild cognitive impairment, and elderly controlsNeuroImage, 2008
- Effects of spatial transformation on regional brain volume estimatesNeuroImage, 2008
- Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structuresNeuroImage, 2007
- On combining classifiersIeee Transactions On Pattern Analysis and Machine Intelligence, 1998
- Measures of the Amount of Ecologic Association Between SpeciesEcology, 1945