Segmentation of brain MRI in young children.
OBJECTIVE
This article deals with an automatic tissue segmentation of brain magnetic resonance imaging (MRI) in young children.


METHODS
We examine the suitability of state-of-the-art methods developed for the adult brain when applied to the segmentation of the brain MRI in young children. We develop a method of creation of a population-specific atlas in young children using a single manual segmentation. The method is based on nonlinear propagation of the segmentation into population and subsequent affine alignment into a reference space and averaging.


RESULTS
Using this approach, we significantly improve the performance of the popular expectation-maximization algorithm on brain MRI in young children. The method can be used for building probabilistic atlases with any number of structures. We compare resulting algorithm with nonrigid registration-based label propagation.


CONCLUSIONS
Finally, both methods are used to measure the volume of seven brain structures and measure the growth between 1 and 2 years of age.
Murgasova
Maria
M
Visual Information Processing Group, Department of Computing, Imperial College, London, UK. maria.murgasova@imperial.ac.uk
Dyet
Leigh
L
Edwards
David
D
Rutherford
Mary
M
Hajnal
Jo
J
Rueckert
Daniel
D
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D007223:Infant; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique