Clinical annotations, such as voxel-wise binary or
probabilistic tissue segmentations, structural parcellations, pathological
regions-of-interest and anatomical landmarks are key to
many clinical studies. However, due to the time consuming nature
of manually generating these annotations, they tend to be scarce
and limited to small subsets of data. This work explores a novel
framework to propagate voxel-wise annotations between morphologically
dissimilar images by diffusing and mapping the available
examples through intermediate steps. A spatially-variant graph
structure connecting morphologically similar subjects is introduced
over a database of images, enabling the gradual diffusion of
information to all the subjects, even in the presence of large-scale
morphological variability. We illustrate the utility of the proposed
framework on two example applications: brain parcellation using
categorical labels and tissue segmentation using probabilistic features.
The application of the proposed method to categorical label
fusion showed highly statistically significant improvements when
compared to state-of-the-art methodologies. Significant improvements
were also observed when applying the proposed framework
to probabilistic tissue segmentation of both synthetic and real data,
mainly in the presence of large morphological variability.