Accessibility navigation

Automated multimodal volume registration based on supervised 3D anatomical landmark detection

Vandaele, R., Lallemand, F., Martineve, P., Gulyban, A., Jodogne, S., Geurts, P., Geurts, P. and Marée, R. (2017) Automated multimodal volume registration based on supervised 3D anatomical landmark detection. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 27-1 Feb 2017, Porto, Portugal, pp. 333-340. (Volume 5)

Text (Open Access) - Published Version
· Available under License Creative Commons Attribution Non-commercial No Derivatives.
· Please see our End User Agreement before downloading.


It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Official URL:


We propose a new method for automatic 3D multimodal registration based on anatomical landmark detection. Landmark detectors are learned independantly in the two imaging modalities using Extremely Randomized Trees and multi-resolution voxel windows. A least-squares fitting algorithm is then used for rigid registration based on the landmark positions as predicted by these detectors in the two imaging modalities. Experiments are carried out with this method on a dataset of pelvis CT and CBCT scans related to 45 patients. On this dataset, our fully automatic approach yields results very competitive with respect to a manually assisted state-of-the-art rigid registration algorithm.

Item Type:Conference or Workshop Item (Paper)
Divisions:No Reading authors. Back catalogue items
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:90836


Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation