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
More filters
Proceedings ArticleDOI
Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images
TL;DR: This work presents a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser, and reports the first qualitative and quantitative assessment of the demons for inter-modal registration.
Journal ArticleDOI
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction
Daniele Ravi,Agnieszka Barbara Szczotka,Dzhoshkun I. Shakir,Stephen P. Pereira,Tom Vercauteren +4 more
TL;DR: In this paper, the authors proposed a novel synthetic data generation approach to train exemplar-based deep neural networks (DNNs) for super-resolution of probe-based confocal laser endomicroscopy (pCLE) images.
Proceedings ArticleDOI
Endomicroscopic image retrieval and classification using invariant visual features
TL;DR: This paper investigates the use of modern content based image retrieval methods to classify endomicroscopic images into two categories: neoplastic (pathological) and benign.
Proceedings ArticleDOI
Real-time mosaicing of fetoscopic videos using SIFT
Pankaj Daga,Francois Chadebecq,Dzhoshkun I. Shakir,Luis Carlos Garcia-Peraza Herrera,Marcel Tella,George Dwyer,Anna L. David,Jan Deprest,Danail Stoyanov,Tom Vercauteren,Sebastien Ourselin +10 more
TL;DR: Initial qualitative results on ex-vivo placental images indicate that the proposed framework can generate clinically useful mosaics from fetoscopic videos in real-time, and leverages the parallelism of modern GPUs and can process clinical fetoscopic images inreal-time.
Journal ArticleDOI
Evaluation of Deformable Image Coregistration in Adaptive Dose Painting by Numbers for Head-and-Neck Cancer
TL;DR: Visual inspection and adjustment were necessary for most ROIs and fast automatic ROI propagation followed by user-driven adjustment appears to be more efficient than labor-intensive de novo drawing.