The corpus callosum (CC) is a major white-matter structure and a well-established marker of brain aging, but most studies quantify it using scalar summaries that discard its boundary geometry. We present a Riemannian shape-space framework for analyzing age-related morphological change in the midsagittal CC, applied to the OASIS-1 cohort. Each contour is represented by
128 landmarks and embedded into Kendall shape space, where translation, rotation, and scale are removed. We derive a multivariate geodesic regression with exact Riemannian gradients and use the fitted age-velocity field to localize age-related deformation to five anatomical sub-regions. In the cognitively normal cohort (
n=252), geodesic regression outperforms the Euclidean linear benchmark (
R2=0.1355 vs.\
0.1216). Regional energy is posterior-dominant: the Splenium carries
41.4% and the Isthmus
23.8% of total age-related shape change, together accounting for
∼65% despite comprising only
∼35% of landmarks. Signed projections confirm the ordering (Splenium
r=0.570; Isthmus
r=0.473). In contrast, age explains less than
0.5% of shape variance in Alzheimer's disease (
n=88), indicating that the disease disrupts the healthy aging trajectory. A tangent-space classifier achieves an age-group AUC of
0.791 from the 2D contour alone, exceeding a recent volumetric benchmark (
0.67).