Hierarchical multi-scale supervoxel matching using random forests for automatic semi-dense abdominal image registration
P.-H. Conze, Florian Tilquin, Vincent Noblet, Francois Waldeck Rousseau, Fabrice Heitz, Patrick Pessaux · 2017
This paper addresses the estimation of pairwise supervoxel correspondences toward automatic semi-dense medical image registration. Supervoxel matching is performed through random forests (RF) with supervoxel indexes as label entities to predict matching areas in another target image. Ensuring accurate supervoxel boundary adherence requires a fine supervoxel decomposition which highly increases learning complexity. To alleviate this issue, we extend RF based supervoxel matching from single to multi-scale using a recursive hierarchical supervoxel representation. Output RF matching probabilities obtained for the last scale are gathered with ancestor matching probabilities which acts as a coarse-to-fine matching guidance. The effectiveness of our method is high-lighted for semi-dense abdominal image registration relying on liver label propagation and consistency assessment.