Automated Multimodal Volume Registration based on Supervised 3D Anatomical Landmark Detection

Rémy Vandaele, François Lallemand, Philippe Martinive, Akos Gulyban, Sébastien Jodogne, Philippe Coucke, Pierre Geurts, Raphaël Marée

2017

Abstract

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.

Paper Citation

in Harvard Style

Vandaele R., Lallemand F., Martinive P., Gulyban A., Jodogne S., Coucke 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 - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 333-340. DOI: 10.5220/0006153803330340

in Bibtex Style

@conference{visapp17,
author={Rémy Vandaele and François Lallemand and Philippe Martinive and Akos Gulyban and Sébastien Jodogne and Philippe Coucke and Pierre Geurts and Raphaël Marée},
title={Automated Multimodal Volume Registration based on Supervised 3D Anatomical Landmark Detection},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={333-340},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006153803330340},
isbn={978-989-758-226-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Automated Multimodal Volume Registration based on Supervised 3D Anatomical Landmark Detection
SN - 978-989-758-226-4
AU - Vandaele R.
AU - Lallemand F.
AU - Martinive P.
AU - Gulyban A.
AU - Jodogne S.
AU - Coucke P.
AU - Geurts P.
AU - Marée R.
PY - 2017
SP - 333
EP - 340
DO - 10.5220/0006153803330340