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
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.
Available from: Wanze Xie
- "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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ABSTRACT: This article summarizes a life-span neurodevelopmental MRI database. The study of neurostructural development or neurofunctional development has been hampered by the lack of age-appropriate MRI reference volumes. This causes misspecification of segmented data, irregular registrations, and the absence of appropriate stereotaxic volumes. We have created the "Neurodevelopmental MRI Database" that provides age-specific reference data from 2weeks through 89years of age. The data are presented in fine-grained ages (e.g., 3months intervals through 1year; 6months intervals through 19.5years; 5year intervals from 20 through 89years). The base component of the database at each age is an age-specific average MRI template. The average MRI templates are accompanied by segmented partial volume estimates for segmenting priors, and a common stereotaxic atlas for infant, pediatric, and adult participants. The database is available online (http://jerlab.psych.sc.edu/NeurodevelopmentalMRIDatabase/).
Copyright © 2015. Published by Elsevier Inc.
Available from: Antonino Vallesi
- " MRIcroN ( http : / / www . mccauslandcenter . sc . edu / mricro / mricron / ) was used to find the likely Brodmann area ( BA ) for each cluster . Similarly , the Hammers - mith n30r83 atlas ( © Copyright Imperial College of Science , Technology and Medi - cine 2007 , All rights reserved ; www . brain - development . org ; Hammers et al . , 2003 ; Gousias et al . , 2008 ) , which is a probabilistic atlas of neuroanatomy , was used to find the likely anatomical region for each cluster ."
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ABSTRACT: Inductive reasoning is an everyday process that allows us to make sense of the world by creating rules from a series of instances. Consistent with accounts of process-based fractionations of the prefrontal cortex (PFC) along the left-right axis, inductive reasoning has been reliably localized to left PFC. However, these results may be confounded by the task domain, which is typically verbal. Indeed, some studies show that right PFC activation is seen with spatial tasks. This study used fMRI to examine the effects of process and domain on the brain regions recruited during a novel pattern discovery task. Twenty healthy young adult participants were asked to discover the rule underlying the presentation of a series of letters in varied spatial locations. The rules were either verbal (pertaining to a single semantic category) or spatial (geometric figures). Bilateral ventrolateral PFC activations were seen for the spatial domain, while the verbal domain showed only left ventrolateral PFC. A conjunction analysis revealed that the two domains recruited a common region of left ventrolateral PFC. The data support a central role of left PFC in inductive reasoning. Importantly, they also suggest that both process and domain shape the localization of reasoning in the brain.
Copyright © 2014. Published by Elsevier Ltd.
Available from: Simon Fristed Eskildsen
- "Ten infant datasets were also downloaded from the brain segmentation testing protocol  website (https://sites.google.com/site/brainseg/). These data were originally collected by Gousias et al.  and are also available at http://www.brain-development.org/ (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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ABSTRACT: Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.
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