Search Results for author: Carole H. Sudre

Found 17 papers, 5 papers with code

A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation

no code implementations6 Sep 2021 Richard Shaw, Carole H. Sudre, Sebastien Ourselin, M. Jorge Cardoso, Hugh G. Pemberton

We aim to automate the process using a probabilistic network that estimates segmentation uncertainty through a heteroscedastic noise model, providing a measure of task-specific quality.

MRI segmentation

Estimating MRI Image Quality via Image Reconstruction Uncertainty

no code implementations21 Jun 2021 Richard Shaw, Carole H. Sudre, Sebastien Ourselin, M. Jorge Cardoso

Thus, we argue that quality control for visual assessment cannot be equated to quality control for algorithmic processing.

Image Quality Assessment Image Reconstruction

Common Limitations of Image Processing Metrics: A Picture Story

1 code implementation12 Apr 2021 Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, M. Jorge Cardoso, Veronika Cheplygina, Beth Cimini, Gary S. Collins, Keyvan Farahani, Ben Glocker, Patrick Godau, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Michael M. Hoffmann, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Alexandros Karargyris, Alan Karthikesalingam, Bernhard Kainz, Emre Kavur, Hannes Kenngott, Jens Kleesiek, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Felix Nickel, Jens Petersen, Gorkem Polat, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clarisa Sanchez Gutierrez, Julien Schroeter, Anindo Saha, Shravya Shetty, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Sotirios A. Tsaftaris, Bram van Ginneken, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Annette Kopp-Schneider, Paul Jäger, Lena Maier-Hein

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.

Instance Segmentation Object Detection +1

Biomechanical modelling of brain atrophy through deep learning

no code implementations14 Dec 2020 Mariana da Silva, Kara Garcia, Carole H. Sudre, Cher Bass, M. Jorge Cardoso, Emma Robinson

We present a proof-of-concept, deep learning (DL) based, differentiable biomechanical model of realistic brain deformations.

Data Augmentation

Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets

1 code implementation2 Nov 2020 Benjamin Murray, Eric Kerfoot, Mark S. Graham, Carole H. Sudre, Erika Molteni, Liane S. Canas, Michela Antonelli, Kerstin Klaser, Alessia Visconti, Andrew T. Chan, Paul W. Franks, Richard Davies, Jonathan Wolf, Tim Spector, Claire J. Steves, Marc Modat, Sebastien Ourselin

We present ExeTera, an open source data curation software designed to address scalability challenges and to enable reproducible research across an international research group for datasets such as the Covid Symptom Study dataset.

Test-time Unsupervised Domain Adaptation

no code implementations5 Oct 2020 Thomas Varsavsky, Mauricio Orbes-Arteaga, Carole H. Sudre, Mark S. Graham, Parashkev Nachev, M. Jorge Cardoso

Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain).

Unsupervised Domain Adaptation

Hierarchical brain parcellation with uncertainty

no code implementations16 Sep 2020 Mark S. Graham, Carole H. Sudre, Thomas Varsavsky, Petru-Daniel Tudosiu, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

We introduce a hierarchically-aware brain parcellation method that works by predicting the decisions at each branch in the label tree.

Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

no code implementations MIDL 2019 Petru-Daniel Tudosiu, Thomas Varsavsky, Richard Shaw, Mark Graham, Parashkev Nachev, Sebastien Ourselin, Carole H. Sudre, M. Jorge Cardoso

The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical volumetric data at higher resolutions.

A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality

no code implementations MIDL 2019 Richard Shaw, Carole H. Sudre, Sebastien Ourselin, M. Jorge Cardoso

By augmenting the training data with different types of simulated k-space artefacts, we propose a novel cascading CNN architecture based on a student-teacher framework to decouple sources of uncertainty related to different k-space augmentations in an entirely self-supervised manner.

Let's agree to disagree: learning highly debatable multirater labelling

no code implementations4 Sep 2019 Carole H. Sudre, Beatriz Gomez Anson, Silvia Ingala, Chris D. Lane, Daniel Jimenez, Lukas Haider, Thomas Varsavsky, Ryutaro Tanno, Lorna Smith, Sébastien Ourselin, Rolf H. Jäger, M. Jorge Cardoso

Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time.

Object Detection

Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning

no code implementations16 Aug 2019 Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H. Sudre, Zach Eaton-Rosen, Lewis J. Haddow, Lauge Sørensen, Mads Nielsen, Akshay Pai, Sébastien Ourselin, Marc Modat, Parashkev Nachev, M. Jorge Cardoso

Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to $n$ target domains (as long as there is paired data covering all domains).

Domain Adaptation

3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects

no code implementations21 Dec 2018 Carole H. Sudre, Beatriz Gomez Anson, Silvia Ingala, Chris D. Lane, Daniel Jimenez, Lukas Haider, Thomas Varsavsky, Lorna Smith, H. Rolf Jäger, M. Jorge Cardoso

Extremely small objects (ESO) have become observable on clinical routine magnetic resonance imaging acquisitions, thanks to a reduction in acquisition time at higher resolution.

PIMMS: Permutation Invariant Multi-Modal Segmentation

no code implementations17 Jul 2018 Thomas Varsavsky, Zach Eaton-Rosen, Carole H. Sudre, Parashkev Nachev, M. Jorge Cardoso

In a research context, image acquisition will often involve a pre-defined static protocol and the data will be of high quality.

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

7 code implementations11 Jul 2017 Carole H. Sudre, Wenqi Li, Tom Vercauteren, Sébastien Ourselin, M. Jorge Cardoso

Deep-learning has proved in recent years to be a powerful tool for image analysis and is now widely used to segment both 2D and 3D medical images.

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