T
Tom Vercauteren
Researcher at King's College London
Publications - 443
Citations - 19699
Tom Vercauteren is an academic researcher from King's College London. The author has contributed to research in topics: Segmentation & Computer science. The author has an hindex of 47, co-authored 381 publications receiving 14216 citations. Previous affiliations of Tom Vercauteren include Mauna Kea Technologies & Wellcome Trust.
Papers
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Journal ArticleDOI
LoViT: Long Video Transformer for Surgical Phase Recognition
Yang Liu,Maxence Boels,Luis C. Garcia-Peraza-Herrera,Tom Vercauteren,Prokar Dasgupta,Alejandro Granados,Sebastien Ourselin +6 more
TL;DR: LoViT as mentioned in this paper combines a temporally-rich spatial feature extractor and a multi-scale temporal aggregator consisting of two cascaded L-Trans modules based on self-attention, followed by a G-Informer module based on ProbSparse selfattention for processing global temporal information.
Proceedings ArticleDOI
Quantifying the Intra-Operative Hemodynamic Effects of Glue Embolization in Vein of Galen Malformations
Premal A. Patel,Dimitra Flouri,Adam Rennie,Fergus Robertson,Lauren Davies,Vijeya Ganesan,Sanjay Bhate,Paolo De Coppi,Tom Vercauteren,Andrew Melbourne +9 more
TL;DR: Results show consistent results including a post-embolization increase in the delay in time of peak contrast density relative to the injected artery at the venous outflow in keeping with reduced shunting and redistribution of blood following embolization.
Journal ArticleDOI
Quantitative analysis of the three dimensional fetoplacental vascular tree in normal, term placenta
Andrew Melbourne,Rosalind Pratt,J. Ciaran Hutchinson,Owen J. Arthurs,Neil J. Sebire,Tom Vercauteren,Anna L. David,Sebastien Ourselin +7 more
Book ChapterDOI
Scale factor point spread function matching: Beyond aliasing in image resampling
TL;DR: In this article, the scale factor point spread function (sfPSF) was proposed to avoid aliasing and redundant oversampling in medical image resampling, which can cope with arbitrary non-linear spatial transformations and grid sizes.
Book ChapterDOI
Bilateral Weighted Adaptive Local Similarity Measure for Registration in Neurosurgery
Martin Kochan,Marc Modat,Tom Vercauteren,Mark White,Mark White,Laura Mancini,Gavin P. Winston,Gavin P. Winston,Andrew W. McEvoy,John S. Thornton,Tarek A. Yousry,John S. Duncan,Sebastien Ourselin,Danail Stoyanov +13 more
TL;DR: This work modifications LNCC to use locally adaptive weighting inspired by bilateral filtering and evaluates it extensively in a numerical phantom study, a clinical iMRI study and a segmentation propagation study, enabling increased registration accuracy near tissue and resection boundaries.