The segmentation of the subcortical structures of the brain is required for many forms of quantitative neuroanatomic
analysis. The volumetric and shape parameters of structures such as lateral ventricles, putamen,
caudate, hippocampus, pallidus and amygdala are employed to characterize a disease or its evolution. This paper
presents a fully automatic segmentation of these structures via a non-rigid registration of a probabilistic atlas
prior and alongside a comprehensive validation.
Our approach is based on an unbiased diffeomorphic atlas with probabilistic spatial priors built from a
training set of MR images with corresponding manual segmentations. The atlas building computes an average
image along with transformation fields mapping each training case to the average image. These transformation
fields are applied to the manually segmented structures of each case in order to obtain a probabilistic map
on the atlas. When applying the atlas for automatic structural segmentation, an MR image is first intensity
inhomogeneity corrected, skull stripped and intensity calibrated to the atlas. Then the atlas image is registered
to the image using an affine followed by a deformable registration matching the gray level intensity. Finally, the
registration transformation is applied to the probabilistic maps of each structures, which are then thresholded
at 0.5 probability.
Using manual segmentations for comparison, measures of volumetric differences show high correlation with
our results. Furthermore, the dice coefficient, which quantifies the volumetric overlap, is higher than 62% for all
structures and is close to 80% for basal ganglia. The intraclass correlation coefficient computed on these same
datasets shows a good inter-method correlation of the volumetric measurements. Using a dataset of a single
patient scanned 10 times on 5 different scanners, reliability is shown with a coefficient of variance of less than 2
percents over the whole dataset. Overall, these validation and reliability studies show that our method accurately
and reliably segments almost all structures. Only the hippocampus and amygdala segmentations exhibit relative
low correlation with the manual segmentation in at least one of the validation studies, whereas they still show
appropriate dice overlap coefficients.