Mapping Genetic Influences on Brain Shape Using Multi-Atlas Fluid Image Alignment

In this pilot study, we developed a set of computer vision based surface segmentation and statistical shape analysis algorithms to study genetic influences on brain structure in a database of brain MRI scans of normal twins. A set of manually delineated 3D parametric surfaces, representing the lateral ventricles, was deformed, using a Navier-Stokes fluid image registration algorithm, onto all the images in the database. The geometric transformations thus obtained were used to propagate the segmentation labels to all the other images. 3D radial distance maps were derived to encode anatomical shape differences. The proportion of shape variance attributable to genetic factors, known as the heritability, was estimated from the shape models using a restricted maximum likelihood method to increase statistical power. Segmentation errors associated with projecting labels onto new images were greatly reduced through multiatlas averaging. The resulting algorithms provide a convenient and sensit...

• Type: Conference
• Year: 2007
• Where: FBIT
• Authors: Meena Mani, Yi-Yu Chou, Natasha Lepore, Agatha D. Lee, Jan de Leeuw, Katie McMahon, Margaret J. Wright, Arthur W. Toga, Paul M. Thompson
• Keywords: Brain Structure, FBIT 2007, Genetic Influences, Information Management, Shape Analysis Algorithms