This article presents a general method for estimating pairwise image correspondences,
which is a fundamental problem in image analysis. The method consists
of over-segmenting a pair of images into supervoxels. A forest classifier is then
trained on one of the images, the source, by using supervoxel indices as voxelwise
class labels. Applying the forest on the other image, the target, yields a
supervoxel labelling, which is then regularised using majority voting within the
boundaries of the target’s supervoxels. This yields semi-dense correspondences
in a fully automatic, unsupervised, efficient and robust manner. The advantage
of our approach is that no prior information or manual annotations are
required, making it suitable as a general initialisation component for various
medical imaging tasks that require coarse correspondences, such as atlas/patchbased
segmentation, registration, and atlas construction. We demonstrate the
effectiveness of our approach in two different applications: a) initialisation of
longitudinal registration on spine CT data of 96 patients, and b) atlas-based
image segmentation using 150 abdominal CT images. Comparison to state-ofthe-art
methods demonstrate the potential of supervoxel classification forests
for estimating image correspondences.