3.1k citations · 5.7k across the 38 of their papers we have counts for
11 papers · 1 filter
Acquisition-invariant brain MRI segmentation with informative uncertainties
Pedro Borges, Richard Shaw, Thomas Varsavsky +5
Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and therefore any down…
The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties
Pedro Borges, Richard Shaw, Thomas Varsavsky +5
Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter depen…
Deep forecasting of translational impact in medical research
Amy PK Nelson, Robert J Gray, James K Ruffle +10
The value of biomedical research--a $1.7 trillion annual investment--is ultimately determined by its downstream, real-world impact. Current objective predictors of impact rest on p…
A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation
Richard Shaw, Carole H. Sudre, Sebastien Ourselin +2
Quality control (QC) of MR images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed visua…
Estimating MRI Image Quality via Image Reconstruction Uncertainty
Richard Shaw, Carole H. Sudre, Sebastien Ourselin +1
Quality control (QC) in medical image analysis is time-consuming and laborious, leading to increased interest in automated methods. However, what is deemed suitable quality for alg…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…
An MRF-UNet Product of Experts for Image Segmentation
Mikael Brudfors, Yaël Balbastre, John Ashburner +4
While convolutional neural networks (CNNs) trained by back-propagation have seen unprecedented success at semantic segmentation tasks, they are known to struggle on out-of-distribu…
Multi-Atlas Based Pathological Stratification of d-TGA Congenital Heart Disease
Maria A. Zuluaga, Alex F. Mendelson, M. Jorge Cardoso +2
One of the main sources of error in multi-atlas segmentation propagation approaches comes from the use of atlas databases that are morphologically dissimilar to the target image. I…
Common Limitations of Image Processing Metrics: A Picture Story
Annika Reinke, Minu D. Tizabi, Carole H. Sudre +90
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are…
Unsupervised Brain Anomaly Detection and Segmentation with Transformers
Walter Hugo Lopez Pinaya, Petru-Daniel Tudosiu, Robert Gray +4
Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific pathological char…
Scale factor point spread function matching: Beyond aliasing in image resampling
M. Jorge Cardoso, Marc Modat, Tom Vercauteren +1
Imaging devices exploit the Nyquist-Shannon sampling theorem to avoid both aliasing and redundant oversampling by design. Conversely, in medical image resampling, images are consid…