Automated multimodal volume registration based on supervised 3D anatomical landmark detectionVandaele, 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)
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Official URL: https://www.scitepress.org/PublicationsDetail.aspx... Abstract/SummaryWe 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.
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