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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

Biomedical image analysis competitions: The state of current participation practice

Matthias Eisenmann, +353 more
- 16 Dec 2022 - 
TL;DR: In this paper , only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%), while 48% of respondents applied postprocessing steps.
Book ChapterDOI

Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning

TL;DR: In this paper, a multi-hypothesis deep learning framework was proposed to generate pseudo CT from MR/CT images by minimising a combination of the pixel-wise error between pCT and CT and a proposed metric loss that itself is represented by a convolutional neural network.
Journal ArticleDOI

Automated postoperative muscle assessment of hip arthroplasty patients using multimodal imaging joint segmentation.

TL;DR: The proposed framework represents a promising tool to support image analysis in hip arthroplasty, being robust to the presence of implants and associated image artefacts and showing stronger association than a single modality approach in a one-way ANOVA F-test analysis.
Proceedings ArticleDOI

Design and Shared Control of a Flexible Endoscope with Autonomous Distal Tip Alignment

TL;DR: A shared control approach is proposed in which the surgeon controls the position of the instrument inside the uterus while an autonomous controller controls the orientation, which is within the targeted range of orientations.
Posted Content

MONAIfbs: MONAI-based fetal brain MRI deep learning segmentation.

TL;DR: In this article, a single-step dynamic UNet (dynUNet) was proposed for segmentation of the fetal brain in Spina bifida. But the performance of the proposed network was not as good as that of the original 2-step approach proposed in Ebner-Wang et al.