Reference : Automated multimodal volume registration based on supervised 3D anatomical landmark d...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
Engineering, computing & technology : Electrical & electronics engineering
Human health sciences : Radiology, nuclear medicine & imaging
http://hdl.handle.net/2268/204352
Automated multimodal volume registration based on supervised 3D anatomical landmark detection
English
[fr] Enregistrement automatisé du volume multimodal basé sur la détection anatomique de repère 3D
Vandaele, Rémy mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
LALLEMAND, François mailto [Centre Hospitalier Universitaire de Liège - CHU > > Radiothérapie >]
MARTINIVE, Philippe mailto [Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiothérapie >]
GULYBAN, Akos mailto [Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiothérapie >]
JODOGNE, Sébastien mailto [Centre Hospitalier Universitaire de Liège - CHU > > Département de Physique Médicale >]
COUCKE, Philippe mailto [Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiothérapie >]
Geurts, Pierre mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique >]
Marée, Raphaël mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
In press
SCITEPRESS Digital Library
Yes
12th International Conference on Computer Vision Theory and Applications (VISAPP 2017)
from 27-02-2017 to 01-03-2017
Porto
Portugal
[en] Registration ; Machine Learning ; Oncology Applications ; Radiation Therapy ; Urology and Pelvic Organs ; Computed Tomography
[en] 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.
Montefiore Institute of Electrical Engineering and Computer Science - Montefiore Institute
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS ; Service public de Wallonie : Direction générale opérationnelle de l'économie, de l'emploi et de la recherche - DG06 ; CECI
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/204352
also: http://hdl.handle.net/2268/207630

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
main.pdfAuthor postprint1.36 MBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.