3.1k citations · 15.9k across the 136 of their papers we have counts for
23 papers · 1 filter
Active Annotation of Informative Overlapping Frames in Video Mosaicking Applications
Loic Peter, Marcel Tella-Amo, Dzhoshkun Ismail Shakir +4
Video mosaicking requires the registration of overlapping frames located at distant timepoints in the sequence to ensure global consistency of the reconstructed scene. However, ful…
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge
Lucas Fidon, Sebastien Ourselin, Tom Vercauteren
Training a deep neural network is an optimization problem with four main ingredients: the design of the deep neural network, the per-sample loss function, the population loss funct…
Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets
Benjamin Murray, Eric Kerfoot, Mark S. Graham +14
The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. Over 4.7 million participants and…
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
Ran Gu, Guotai Wang, Tao Song +6
Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance f…
Hierarchical brain parcellation with uncertainty
Mark S. Graham, Carole H. Sudre, Thomas Varsavsky +4
Many atlases used for brain parcellation are hierarchically organised, progressively dividing the brain into smaller sub-regions. However, state-of-the-art parcellation methods ten…
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
Reuben Dorent, Thomas Booth, Wenqi Li +5
Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been de…
Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking
Luis C. García-Peraza-Herrera, Wenqi Li, Caspar Gruijthuijsen +7
Real-time tool segmentation is an essential component in computer-assisted surgical systems. We propose a novel real-time automatic method based on Fully Convolutional Networks (FC…
Scribble-based Domain Adaptation via Co-segmentation
Reuben Dorent, Samuel Joutard, Jonathan Shapey +7
Although deep convolutional networks have reached state-of-the-art performance in many medical image segmentation tasks, they have typically demonstrated poor generalisation capabi…
Enhancing Fiber Orientation Distributions using convolutional Neural Networks
Oeslle Lucena, Sjoerd B. Vos, Vejay Vakharia +4
Accurate local fiber orientation distribution (FOD) modeling based on diffusion magnetic resonance imaging (dMRI) capable of resolving complex fiber configurations benefits from sp…
Deep Placental Vessel Segmentation for Fetoscopic Mosaicking
Sophia Bano, Francisco Vasconcelos, Luke M. Shepherd +6
During fetoscopic laser photocoagulation, a treatment for twin-to-twin transfusion syndrome (TTTS), the clinician first identifies abnormal placental vascular connections and laser…
Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices
Guotai Wang, Michael Aertsen, Jan Deprest +3
Segmentation of the fetal brain from stacks of motion-corrupted fetal MRI slices is important for motion correction and high-resolution volume reconstruction. Although Convolutiona…
Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning
Fernando Pérez-García, Roman Rodionov, Ali Alim-Marvasti +3
Resective surgery may be curative for drug-resistant focal epilepsy, but only 40% to 70% of patients achieve seizure freedom after surgery. Retrospective quantitative analysis coul…
Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI
Hari McGrath, Peichao Li, Reuben Dorent +6
Management of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. Current clinical practice is to measure the diameter of…
Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework
Marta B. M. Ranzini, Irme Groothuis, Kerstin Kläser +5
Metal artefact reduction (MAR) techniques aim at removing metal-induced noise from clinical images. In Computed Tomography (CT), supervised deep learning approaches have been shown…
Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI
Maria A. Zuluaga, M. Jorge Cardoso, Sébastien Ourselin
Accurate segmentation of the right ventricle (RV) is a crucial step in the assessment of the ventricular structure and function. Yet, due to its complex anatomy and motion segmenta…
The Future of Digital Health with Federated Learning
Nicola Rieke, Jonny Hancox, Wenqi Li +14
Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern…
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Fernando Pérez-García, Rachel Sparks, Sébastien Ourselin
Processing of medical images such as MRI or CT presents unique challenges compared to RGB images typically used in computer vision. These include a lack of labels for large dataset…
Seeing through multimode fibers with real-valued intensity transmission matrices
Tianrui Zhao, Sebastien Ourselin, Tom Vercauteren +1
Image transmission through multimode optical fibers has been an area of immense interests driven by the demand for miniature endoscopes in biomedicine and higher speed and capacity…
Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE
Petru-Daniel Tudosiu, Thomas Varsavsky, Richard Shaw +5
The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical vo…
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +93
We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international…
A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality
Richard Shaw, Carole H. Sudre, Sebastien Ourselin +1
Quality control (QC) of medical images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed…
Physics-informed brain MRI segmentation
Pedro Borges, Carole Sudre, Thomas Varsavsky +4
Magnetic Resonance Imaging (MRI) is one of the most flexible and powerful medical imaging modalities. This flexibility does however come at a cost; MRI images acquired at different…
Distributionally Robust Deep Learning using Hardness Weighted Sampling
Lucas Fidon, Michael Aertsen, Thomas Deprest +14
Limiting failures of machine learning systems is of paramount importance for safety-critical applications. In order to improve the robustness of machine learning systems, Distribut…