Improved segmentation of white matter tracts with adaptive Riemannian metrics
We develop a new Riemannian metric that resolves high-curvature tracts deviation. The adaptive metric is computed by solving a variational problem on the manifold. Our method computes geodesics that more faithfully follow the principal eigenvectors. We present an automatic segmentation method that does not require user intervention. Our method improves segmentations of white matter tracts, including crossing tracts.
geodesic diffusion tensor imaging riemannian manifold conformal factor front-propagation