Published in the proceedings of the 4-th International Conference on Functional Mapping of the Human Brain

Automatic volume estimation of gross cerebral structures $^\dagger$

DL Collins, NJ Kabani, AC Evans

McConnell Brain Imaging Centre, Montréal Neurological Institute,
McGill University, Montréal, Canada

Introduction

The volume of a number of specific cerebral structures have been correlated with different pathologies such as epilepsy, schizophrenia, Alzheimer's disease, multiple infarct dementia and hydrocephalus. To determine structural abnormality, comparisons to a normative data-base are required. However, estimation of normal population parameters is a daunting task since manual structure segmentation is time-consuming, error-prone and inter- and intra-observer variabilities may confound the estimation of true structure variability. To address these problems, we have developed a fully automatic hybrid segmentation scheme based on ANIMAL [1] (non-linear registration) and INSECT [2] (tissue classification) that can be applied to large ensembles of MRI volumes.

Methods

Automatic segmentation is achieved by estimating the non-linear spatial transformation required to register all voxels from a subject's MRI volume with an average MRI brain that is co-registered with a SPAM (Statistical Probability Anatomy Maps) Atlas in a Talairach-like stereotaxic space [3]. The atlas' 90 average gross anatomical structures are mapped through the inverse transform to effectively define customized masks on the subject's MRI for the most-likely region for each structure. Tissue classes such as grey-matter, white-matter and CSF, identified by a minimum distance classifier, are masked by these regions to complete the segmentation. This methodology was applied to the MRI volumes of 152 normal subjects (86 male, 66 female, age $24.6\pm4.8$) as part of the ICBM project.

Results

In the following table, all volumes are in cm³. Values (mean $\pm$standard deviation) for left side precede the right for symmetric structures. Significant differences (p<0.01, two-tailed Student's t-test with Bonferoni correction) are indicated with '<' (right greater than left) or '>' (left greater than right). The left-right volume difference for temporal and parietal lobes are significant at the p=0.05 level.

frontal lobe $[175.0\pm25.3=174.0\pm25.0]$	precentral $[14.5 \pm 2.2 < 16.8 \pm 2.6]$; superior-frontal (fr) $[11.2 \pm 1.4 = 11.2 \pm 1.5]$; middle-fr $[24.9 \pm 3.5 = 22.7 \pm 3.1]$; inferior-fr $[11.4 \pm 1.7 = 12.8 \pm 1.9]$; medial-fr $[14.1 \pm 1.8 = 12.9 \pm 1.8]$; lateral-fronto-orbital (fo) $[9.2 \pm 1.4 = 10.1 \pm 1.5]$; medial-fo $[2.2 \pm 0.4 = 2.6 \pm 0.4]$
temporal lobe $[119.3\pm18.1 = 109.8\pm16.4]$	superior-temporal (t) $[16.4 \pm 2.6 = 16.1 \pm 2.7 ]$; middle-t $[15.5 \pm 2.2 < 23.3 \pm 3.9 ]$; inferior-t $[6.8 \pm 1.4 = 7.5 \pm 1.3]$; medial occipito-temporal (ot) $[7.0 \pm 1.0 = 6.4 \pm 1.0]$; lateral-ot $[12.1 \pm 1.9 = 11.0 \pm 1.7]$; uncus $[2.5 \pm 0.5 = 2.7 \pm 0.7 ]$; parahippocampal $[4.4 \pm 0.8 = 5.4 \pm 1.0 ]$
parietal lobe $[95.0\pm13.7=99.3\pm14.3]$	postcentral $[15.6 \pm 2.3 > 11.1 \pm 1.8 ]$; superior parietal lobule $[14.4 \pm 2.2 = 16.6 \pm 2.4 ]$; supramarginal $[7.1 \pm 1.2 = 5.3 \pm 0.9 ]$; angular $[7.8 \pm 1.2 < 11.0 \pm 1.7]$; precuneus $[4.4 \pm 0.8 = 4.4 \pm 0.8]$
occipital lobe $[44.8\pm7.9 = 45.9\pm8.2]$	superior-occipital (o) $[5.2 \pm 1.0 = 5.0 \pm 1.0]$; middle-o $[4.3 \pm 0.7 = 5.3 \pm 0.9]$; inferior-o $[4.0 \pm 0.8 > 1.9 \pm 0.4]$; occipital pole $[3.3 \pm 0.9 = 2.8 \pm 0.7 ]$; cuneus $[5.5 \pm 1.1 = 5.6 \pm 1.0 ]$; lingual $[4.7 \pm 1.2 = 3.2 \pm 0.8]$
other grey	insula $[8.8 \pm 1.1 = 8.8 \pm 1.1 ]$; cingulate $[17.1 \pm 2.4 = 14.9 \pm 2.0 ]$; hippocampus $[5.1 \pm 0.8 = 4.0 \pm 0.6 ]$; caudate $[5.4 \pm 0.7 = 5.1 \pm 0.6 ]$; putamen $[5.4 \pm 0.7 = 5.5 \pm 0.7 ]$; globus pallidus $[0.9 \pm 0.2 = 1.4 \pm 0.2 ]$; thalamus $[8.3 \pm 0.9 = 9.0 \pm 1.0 ]$; nucleus accumbens $[0.3 \pm 0.1 = 0.4 \pm 0.1 ]$; subthalamic nucleus $[0.1 \pm 0.0 = 0.1 \pm 0.0 ]$
other	corpus collosum $[10.9 \pm 1.5]$; fornix $[0.3 \pm 0.1 ]$; anterior internal capsule (ic) $[3.3 \pm 0.5 = 3.0 \pm 0.6]$; posterior ic $[1.6 \pm 0.3 = 1.6 \pm 0.3 ]$; lateral ventricle (v) $[8.9 \pm 3.6 = 8.0 \pm 3.4 ]$;

Conclusion

The method presented here is completely automatic, fully objective, and has been applied to a large ensemble of brain volumes. The resulting volume statistics will prove useful as a normative data base for comparisons in future studies of normal or pathological brains.

Bibliography

  1. D.L. Collins, C.J. Holmes, T.M. Peters and A.C. Evans. Human Brain Mapping. 3(3):190-208, 1996.

  2. Zijdenbos, A.P., Evans, A.C., Riahi, et al. Proc Vis Biomed Comp. p 439-448 1996.

  3. A.C. Evans, D.L. Collins, C.J. Holmes, et al. ``Towards a probabilistic atlas of human neuroanatomy'' in Brain Mapping: The Methods. 1996

$^\dagger$Supported by the U.S. Human Brain Map Project and the International Consortium for Brain Mapping.