A robust model for the automatic segmentation of human brain images into anatomically defined regions across the
human lifespan would be highly desirable, but such structural segmentations of brain MRI are challenging due to age-related
changes. We have developed a new method, based on established algorithms for automatic segmentation of
young adults' brains. We used prior information from 30 anatomical atlases, which had been manually segmented into
83 anatomical structures. Target MRIs came from 80 subjects (~12 individuals/decade) from 20 to 90 years, with equal
numbers of men, women; data from two different scanners (1.5T, 3T), using the IXI database. Each of the adult atlases
was registered to each target MR image. By using additional information from segmentation into tissue classes (GM,
WM and CSF) to initialise the warping based on label consistency similarity before feeding this into the previous
normalised mutual information non-rigid registration, the registration became robust enough to accommodate atrophy
and ventricular enlargement with age. The final segmentation was obtained by combination of the 30 propagated atlases
using decision fusion. Kernel smoothing was used for modelling the structural volume changes with aging. Example
linear correlation coefficients with age were, for lateral ventricular volume, rmale=0.76, rfemale=0.58 and, for hippocampal
volume, rmale=-0.6, rfemale=-0.4 (allρ<0.01).
In this work we obtain estimates of tissue growth using longitudinal data comprising MR brain images
of 25 preterm children scanned at one and two years. The growth estimates are obtained using segmentation
and registration based methods. The segmentation approach used an expectation maximisation
(EM) method to classify tissue types and the registration approach used tensor based morphometry
(TBM) applied to a free form deformation (FFD) model. The two methods show very good agreement
indicating that the registration and segmentation approaches can be used interchangeably. The advantage
of the registration based method, however, is that it can provide more local estimates of tissue
growth. This is the first longitudinal study of growth in early childhood, previous longitudinal studies
have focused on later periods during childhood.
Conference Committee Involvement (5)
Image Processing Posters
12 February 2017 | Orlando, FL, United States
Image Processing
12 February 2017 | Orlando, Florida, United States
Image Processing
1 March 2016 | San Diego, California, United States
Image Processing
24 February 2015 | Orlando, Florida, United States
Image Processing
16 February 2014 | San Diego, California, United States
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