Voxel-wise correspondence of cone-beam computed tomography images by cascaded randomized forest

This paper addresses a dense voxel-wise correspondence of cone-beam computed tomography (CBCT) images towards a non-rigid registration and treatments evaluation in clinical orthodontics. An unsupervised clustering randomized forest is employed to establish voxel-wise correspondence in a reduced subset of the original volume image. A geodesic coordinate is introduced to avoid the structural ambiguities. The geodesic coordinates updated with voxel-wise affinities yield a cascaded geodesic forest. Given a novel volume image, the appearance and cascaded geodesic forests produce a voxel-wise correspondence in the subsets. A regularization scheme is employed to propagate the subset correspondence to the whole images, which results in a dense displacement field of the non-rigid registration between the reference and target volume images. Our technique is based on the unsupervised clustering forests and does not need the predefined atlas for training. Quantitative assessment on practitioner-annotated ground truth demonstrates an improvement to the state-of-the-arts label propagation techniques of CBCT images.