The application of the tracking-learning-detection (TLD) framework; a performant tracking algorithm for real-life objects in CCD video, was evaluated and successfully optimized for tracking anatomical structures in low-quality 2D cine-MRI acquired during MRI-guided radiotherapy. Sub-pixel tracking accuracy and >95% precision and recall was achieved despite significant deformations and periodical disappearance.
Dhont, J, Vandemeulebroucke, J, Cusumano, D, Boldrini, L, Cellini, F, Valentini, V & Verellen, D 2019, 'Multi-object tracking in MRI-guided radiotherapy using the tracking-learning-detection framework.', Radiother Oncol, vol. 138, pp. 25-29. https://doi.org/10.1016/j.radonc.2019.05.008
Dhont, J., Vandemeulebroucke, J., Cusumano, D., Boldrini, L., Cellini, F., Valentini, V., & Verellen, D. (2019). Multi-object tracking in MRI-guided radiotherapy using the tracking-learning-detection framework. Radiother Oncol, 138, 25-29. https://doi.org/10.1016/j.radonc.2019.05.008
@article{9f86186dfcf2493785fb25fde50b47b9,
title = "Multi-object tracking in MRI-guided radiotherapy using the tracking-learning-detection framework.",
abstract = "The application of the tracking-learning-detection (TLD) framework; a performant tracking algorithm for real-life objects in CCD video, was evaluated and successfully optimized for tracking anatomical structures in low-quality 2D cine-MRI acquired during MRI-guided radiotherapy. Sub-pixel tracking accuracy and >95% precision and recall was achieved despite significant deformations and periodical disappearance.",
author = "Jennifer Dhont and Jef Vandemeulebroucke and D. Cusumano and L. Boldrini and F. Cellini and V Valentini and Dirk Verellen",
year = "2019",
month = sep,
doi = "10.1016/j.radonc.2019.05.008",
language = "English",
volume = "138",
pages = "25--29",
journal = "Radiother Oncol",
issn = "0167-8140",
publisher = "Elsevier Ireland Ltd",
}