Automatic segmentation of brain MRIs of 2-year-olds in 83 regions of interest

Imaging Sciences Department, MRC Clinical Sciences Centre and Department of Pediatrics, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
NeuroImage (Impact Factor: 6.36). 05/2008; 40(2):672-84. DOI: 10.1016/j.neuroimage.2007.11.034
Source: PubMed


Three-dimensional atlases and databases of the brain at different ages facilitate the description of neuroanatomy and the monitoring of cerebral growth and development. Brain segmentation is challenging in young children due to structural differences compared to adults. We have developed a method, based on established algorithms, for automatic segmentation of young children's brains into 83 regions of interest (ROIs), and applied this to an exemplar group of 33 2-year-old subjects who had been born prematurely. The algorithm uses prior information from 30 normal adult brain magnetic resonance (MR) images, which had been manually segmented to create 30 atlases, each labeling 83 anatomical structures. Each of these adult atlases was registered to each 2-year-old target MR image using non-rigid registration based on free-form deformations. Label propagation from each adult atlas yielded a segmentation of each 2-year-old brain into 83 ROIs. The final segmentation was obtained by combination of the 30 propagated adult atlases using decision fusion, improving accuracy over individual propagations. We validated this algorithm by comparing the automatic approach with three representative manually segmented volumetric regions (the subcortical caudate nucleus, the neocortical pre-central gyrus and the archicortical hippocampus) using similarity indices (SI), a measure of spatial overlap (intersection over average). SI results for automatic versus manual segmentations for these three structures were 0.90+/-0.01, 0.90+/-0.01 and 0.88+/-0.03 respectively. This registration approach allows the rapid construction of automatically labelled age-specific brain atlases for children at the age of 2 years.

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    • "e space , and partial volume probabilities were constructed for each segmented material for each average . The stereotaxic atlases were constructed from manually segmented lobar atlas for selected averages ( e . g . , all first year ; and 6 , 12 , 18 , and 20 – 24 year average 3 T templates ) , with image fusion methods ( e . g . , majority vote , Gousias et al . , 2008 ; joint fusion , Wang et al . , 2013 ) for the LONI LPBA40 ( LONI Probabilistic Brain Atlas , LPBA40 , Shattuck et al . , 2008 ) and the IXI Hammers ( Hammers atlases ; Hammers et al . , 2003 ; Heckemann et al . , 2006 ; Heckemann et al . , 2003 ) atlases for all 3 T average templates ( 4 years through 30 – 34 year templates ; e . g . ,"
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    • "Ten infant datasets were also downloaded from the brain segmentation testing protocol [18] website ( These data were originally collected by Gousias et al. [19] and are also available at (this dataset is property of the Imperial College of Science Technology & Medicine and has been used after accepting the license agreement). "
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