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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

Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.

Arno Klein, +21 more
- 01 Jul 2009 - 
TL;DR: This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted and suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols.
Journal ArticleDOI

Diffeomorphic demons: efficient non-parametric image registration.

Tom Vercauteren, +3 more
- 01 Mar 2009 - 
TL;DR: An efficient non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm that provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the gold standard, available in controlled experiments, in terms of Jacobians.
Book ChapterDOI

Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations

Carole H. Sudre, +7 more
TL;DR: In this paper, the authors investigate the behavior of these loss functions and their sensitivity to learning rate tuning in the presence of different rates of label imbalance across 2D and 3D segmentation tasks.
Posted ContentDOI

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Journal ArticleDOI

Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning

Guotai Wang, +10 more
- 26 Jan 2018 - 
TL;DR: A novel deep learning-based interactive segmentation framework by incorporating CNNs into a bounding box and scribble-based segmentation pipeline and proposing a weighted loss function considering network and interaction-based uncertainty for the fine tuning is proposed.