Adaptive Weighted Least Squares Conformal Mapping and Cortical
       Surface Flattening

Abstract:
Although flattening a cortical surface necessarily introduces metric
distortion due to the non-constant Gaussian curvature of the surface,
the Riemann Mapping Theorem states that continuously differentiable
surfaces can be mapped without local angular distortion. In this talk,
we will discuss the so-called least-squares conformal mapping and its
adaptive approaches for cortical surface flattening. Some comparisons
with other flattening techniques will also be presented.