Search Results for author: M. Jorge Cardoso

Found 47 papers, 8 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

Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks

no code implementations18 Aug 2021 Mariana da Silva, Carole H. Sudre, Kara Garcia, Cher Bass, M. Jorge Cardoso, Emma C. Robinson

Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution.

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

An MRF-UNet Product of Experts for Image Segmentation

1 code implementation12 Apr 2021 Mikael Brudfors, Yaël Balbastre, John Ashburner, Geraint Rees, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso

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-distribution data.

Semantic Segmentation

Multi-Atlas Based Pathological Stratification of d-TGA Congenital Heart Disease

no code implementations5 Apr 2021 Maria A. Zuluaga, Alex F. Mendelson, M. Jorge Cardoso, Andrew M. Taylor, Sébastien Ourselin

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.

Unsupervised Brain Anomaly Detection and Segmentation with Transformers

no code implementations23 Feb 2021 Walter Hugo Lopez Pinaya, Petru-Daniel Tudosiu, Robert Gray, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

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 characteristic.

Unsupervised Anomaly Detection

Scale factor point spread function matching: Beyond aliasing in image resampling

no code implementations16 Jan 2021 M. Jorge Cardoso, Marc Modat, Tom Vercauteren, Sebastien Ourselin

Imaging devices exploit the Nyquist-Shannon sampling theorem to avoid both aliasing and redundant oversampling by design.

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

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.

Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI

no code implementations5 Apr 2020 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.

Motion Segmentation Right Ventricle Segmentation

The Future of Digital Health with Federated Learning

no code implementations18 Mar 2020 Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett Landman, Klaus Maier-Hein, Sebastien Ourselin, Micah Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso

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 healthcare systems.

Federated Learning

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

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

no code implementations21 Aug 2019 Kerstin Kläser, Thomas Varsavsky, Pawel Markiewicz, Tom Vercauteren, David Atkinson, Kris Thielemans, Brian Hutton, M. Jorge Cardoso, Sebastien Ourselin

Quantitative results show that the network generates pCTs that seem less accurate when evaluating the Mean Absolute Error on the pCT (69. 68HU) compared to a baseline CNN (66. 25HU), but lead to significant improvement in the PET reconstruction - 115a. u.

Imitation Learning

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

Robust parametric modeling of Alzheimer's disease progression

no code implementations14 Aug 2019 Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, Marc Modat, M. Jorge Cardoso, Sébastien Ourselin, Lauge Sørensen

Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric MRI and PET biomarkers, CSF measurements, as well as cognitive tests.

Density Estimation

As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging

no code implementations25 Jul 2019 Zach Eaton-Rosen, Thomas Varsavsky, Sebastien Ourselin, M. Jorge Cardoso

Counting is a fundamental task in biomedical imaging and count is an important biomarker in a number of conditions.

Medical Imaging with Deep Learning: MIDL 2019 -- Extended Abstract Track

no code implementations21 May 2019 M. Jorge Cardoso, Aasa Feragen, Ben Glocker, Ender Konukoglu, Ipek Oguz, Gozde Unal, Tom Vercauteren

This compendium gathers all the accepted extended abstracts from the Second International Conference on Medical Imaging with Deep Learning (MIDL 2019), held in London, UK, 8-10 July 2019.

Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling

no code implementations17 Mar 2019 Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Lauge Sørensen

The proposed LSTM algorithm is applied to model the progression of Alzheimer's disease (AD) using six volumetric magnetic resonance imaging (MRI) biomarkers, i. e., volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is compared to standard LSTM networks with data imputation and a parametric, regression-based DPM method.

Hippocampus Imputation

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.

PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation

no code implementations3 Oct 2018 Mauricio Orbes Arteaga, Lauge Sørensen, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Stefan Sommer, Mads Nielsen, Christian Igel, Akshay Pai

For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input.

Medical Image Segmentation

Elastic Registration of Geodesic Vascular Graphs

no code implementations14 Sep 2018 Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jager, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

Vascular graphs can embed a number of high-level features, from morphological parameters, to functional biomarkers, and represent an invaluable tool for longitudinal and cross-sectional clinical inference.

Graph Matching

Deep Boosted Regression for MR to CT Synthesis

no code implementations22 Aug 2018 Kerstin Kläser, Pawel Markiewicz, Marta Ranzini, Wenqi Li, Marc Modat, Brian F. Hutton, David Atkinson, Kris Thielemans, M. Jorge Cardoso, Sebastien Ourselin

Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification.

Computed Tomography (CT) Image Reconstruction

Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs

no code implementations20 Aug 2018 Mauricio Orbes-Arteaga, M. Jorge Cardoso, Lauge Sørensen, Marc Modat, Sébastien Ourselin, Mads Nielsen, Akshay Pai

Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid attenuated inversion recovery (FLAIR) sequence --where lesions appear hyperintense--.

Imputation

Robust training of recurrent neural networks to handle missing data for disease progression modeling

no code implementations16 Aug 2018 Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Lauge Sørensen

This paper shows that built-in handling of missing values in LSTM network training paves the way for application of RNNs in disease progression modeling.

Image Classification Imputation

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.

Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions

no code implementations22 Jun 2018 Zach Eaton-Rosen, Felix Bragman, Sotirios Bisdas, Sebastien Ourselin, M. Jorge Cardoso

Automated medical image segmentation, specifically using deep learning, has shown outstanding performance in semantic segmentation tasks.

Medical Image Segmentation

VTrails: Inferring Vessels with Geodesic Connectivity Trees

no code implementations8 Jun 2018 Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jäger, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso

The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale.

Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction

no code implementations NeuroImage 2018 Claudia Blaiotta, Patrick Freund, M. Jorge Cardoso, John Ashburner

In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations.

Diffeomorphic Medical Image Registration Image Registration

NiftyNet: a deep-learning platform for medical imaging

10 code implementations11 Sep 2017 Eli Gibson, Wenqi Li, Carole Sudre, Lucas Fidon, Dzhoshkun I. Shakir, Guotai Wang, Zach Eaton-Rosen, Robert Gray, Tom Doel, Yipeng Hu, Tom Whyntie, Parashkev Nachev, Marc Modat, Dean C. Barratt, Sébastien Ourselin, M. Jorge Cardoso, Tom Vercauteren

NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications.

Data Augmentation Image Generation +2

An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

no code implementations8 Sep 2017 Lorenz Berger, Eoin Hyde, M. Jorge Cardoso, Sebastien Ourselin

Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation.

Object Recognition Semantic Segmentation

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.

On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

4 code implementations6 Jul 2017 Wenqi Li, Guotai Wang, Lucas Fidon, Sebastien Ourselin, M. Jorge Cardoso, Tom Vercauteren

To illustrate its efficiency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images.

3D Medical Imaging Segmentation Transfer Learning +1

Generative diffeomorphic atlas construction from brain and spinal cord MRI data

no code implementations5 Jul 2017 Claudia Blaiotta, Patrick Freund, M. Jorge Cardoso, John Ashburner

In this paper we will focus on the potential and on the challenges associated with the development of an integrated brain and spinal cord modelling framework for processing MR neuroimaging data.

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