AUTOMATED 3D MAPPING & SHAPE ANALYSIS OF THE LATERAL VENTRICLES VIA FLUID REGISTRATION OF MULTIPLE SURFACE-BASED ATLASES

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles, designed for monitoring degenerative disease effects in clinical neuroscience studies and drug trials. First we used a set of parameterized surfaces to represent the ventricles in a ma...

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Published in2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro pp. 1288 - 1291
Main Authors Yi-Yu Chou, Lepore, N., de Zubicaray, G.I., Rose, S.E., Carmichael, O.T., Becker, J.T., Toga, A.W., Thompson, P.M.
Format Conference Proceeding
LanguageEnglish
Published 01.04.2007
Subjects
Alzheimer's disease
Computerized monitoring
Condition monitoring
Degenerative diseases
Drugs
Image segmentation
Magnetic resonance imaging
Neuroscience
Senior citizens
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Summary:We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles, designed for monitoring degenerative disease effects in clinical neuroscience studies and drug trials. First we used a set of parameterized surfaces to represent the ventricles in a manually labeled set of 9 subjects' MRIs (atlases). We fluidly registered each of these atlases and mesh models to a set of MRIs from 12 Alzheimer's disease (AD) patients and 14 matched healthy elderly subjects, and we averaged the resulting meshes for each of these images. Validation experiments on expert segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease-related alterations monotonically improved as the number of atlases, N, was increased from 1 to 9. We then combined the segmentations with a radial mapping approach to localize ventricular shape differences in patients. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases, and we formulated a statistical stopping criterion to determine the optimal value of N. Anterior horn anomalies in Alzheimer's patients were only detected with the multi-atlas segmentation, which clearly outperformed the standard single-atlas approach.
ISBN:1424406714
9781424406715
ISSN:1945-7928
DOI:10.1109/ISBI.2007.357095