Search Results for author: Andrew P. King

Found 34 papers, 3 papers with code

Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging

no code implementations28 Sep 2022 Emily Chan, Ciaran O'Hanlon, Carlota Asegurado Marquez, Marwenie Petalcorin, Jorge Mariscal-Harana, Haotian Gu, Raymond J. Kim, Robert M. Judd, Phil Chowienczyk, Julia A. Schnabel, Reza Razavi, Andrew P. King, Bram Ruijsink, Esther Puyol-Antón

Flow analysis carried out using phase contrast cardiac magnetic resonance imaging (PC-CMR) enables the quantification of important parameters that are used in the assessment of cardiovascular function.

A systematic study of race and sex bias in CNN-based cardiac MR segmentation

no code implementations4 Sep 2022 Tiarna Lee, Esther Puyol-Anton, Bram Ruijsink, Miaojing Shi, Andrew P. King

We present the first systematic study of the impact of training set imbalance on race and sex bias in CNN-based segmentation.

Management

A Study of Demographic Bias in CNN-based Brain MR Segmentation

no code implementations13 Aug 2022 Stefanos Ioannou, Hana Chockler, Alexander Hammers, Andrew P. King

We find significant sex and race bias effects in segmentation model performance.

Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions

no code implementations5 Aug 2022 Germain Morilhat, Naomi Kifle, Sandra FinesilverSmith, Bram Ruijsink, Vittoria Vergani, Habtamu Tegegne Desita, Zerubabel Tegegne Desita, Esther Puyol-Anton, Aaron Carass, Andrew P. King

This work showcases, for the first time, the potential of DL in automating the process of effusion assessment from ultrasound in LMIC settings where there is often a lack of experienced radiologists to perform such tasks.

AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography

1 code implementation21 Mar 2022 Esther Puyol-Antón, Bram Ruijsink, Baldeep S. Sidhu, Justin Gould, Bradley Porter, Mark K. Elliott, Vishal Mehta, Haotian Gu, Miguel Xochicale, Alberto Gomez, Christopher A. Rinaldi, Martin Cowie, Phil Chowienczyk, Reza Razavi, Andrew P. King

In this work we propose for the first time an AI approach for deriving advanced biomarkers of systolic and diastolic LV function from 2-D echocardiography based on segmentations of the full cardiac cycle.

Management

Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction

no code implementations22 Sep 2021 Tareen Dawood, Chen Chen, Robin Andlauer, Baldeep S. Sidhu, Bram Ruijsink, Justin Gould, Bradley Porter, Mark Elliott, Vishal Mehta, C. Aldo Rinaldi, Esther Puyol-Antón, Reza Razavi, Andrew P. King

Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthcare.

Improved AI-based segmentation of apical and basal slices from clinical cine CMR

no code implementations20 Sep 2021 Jorge Mariscal-Harana, Naomi Kifle, Reza Razavi, Andrew P. King, Bram Ruijsink, Esther Puyol-Antón

Using manual segmentations as a reference, CMR slices were assigned to one of four regions: non-cardiac, base, middle, and apex.

Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data

no code implementations16 Sep 2021 Ines Machado, Esther Puyol-Anton, Kerstin Hammernik, Gastao Cruz, Devran Ugurlu, Bram Ruijsink, Miguel Castelo-Branco, Alistair Young, Claudia Prieto, Julia A. Schnabel, Andrew P. King

The framework consists of a deep learning model for the reconstruction of 2D+t cardiac cine MRI images from undersampled data, an image quality-control step to detect good quality reconstructions, followed by a deep learning model for bi-ventricular segmentation, a quality-control step to detect good quality segmentations and automated calculation of cardiac functional parameters.

MRI Reconstruction

A Multimodal Deep Learning Model for Cardiac Resynchronisation Therapy Response Prediction

no code implementations20 Jul 2021 Esther Puyol-Antón, Baldeep S. Sidhu, Justin Gould, Bradley Porter, Mark K. Elliott, Vishal Mehta, Christopher A. Rinaldi, Andrew P. King

Next, a multimodal deep learning classifier is used for CRT response prediction, which combines the latent spaces of the segmentation models of the two modalities.

Multimodal Deep Learning Specificity

Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation

no code implementations23 Jun 2021 Esther Puyol-Anton, Bram Ruijsink, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Reza Razavi, Andrew P. King

The subject of "fairness" in artificial intelligence (AI) refers to assessing AI algorithms for potential bias based on demographic characteristics such as race and gender, and the development of algorithms to address this bias.

Fairness Meta-Learning

Channel Attention Networks for Robust MR Fingerprinting Matching

no code implementations2 Dec 2020 Refik Soyak, Ebru Navruz, Eda Ozgu Ersoy, Gastao Cruz, Claudia Prieto, Andrew P. King, Devrim Unay, Ilkay Oksuz

Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times.

Magnetic Resonance Fingerprinting

Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients

no code implementations6 Sep 2020 Zhen Yuan, Esther Puyol-Anton, Haran Jogeesvaran, Catriona Reid, Baba Inusa, Andrew P. King

To the best of our knowledge, this is the first attempt to measure spleen size in a fully automated way from ultrasound images.

Quality-aware semi-supervised learning for CMR segmentation

no code implementations1 Sep 2020 Bram Ruijsink, Esther Puyol-Anton, Ye Li, Wenja Bai, Eric Kerfoot, Reza Razavi, Andrew P. King

SemiQCSeg can be an efficient approach for training segmentation networks for medical image data when labelled datasets are scarce.

Data Augmentation Image Segmentation +2

A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI

no code implementations21 Aug 2020 Nick Byrne, James R. Clough, Giovanni Montana, Andrew P. King

With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter observer variation.

Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations

no code implementations2 May 2020 Christopher J Arthurs, Andrew P. King

The contributions of this work are threefold: 1) To demonstrate that neural networks can be efficient aggregators of whole families of parameteric solutions to physical problems, trained using data created with traditional, trusted numerical methods such as finite elements.

Active Learning Data Compression +1

Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation

no code implementations11 Oct 2019 Ilkay Oksuz, James R. Clough, Bram Ruijsink, Esther Puyol Anton, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Andrew P. King, Julia A. Schnabel

In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity.

Image Reconstruction Image Segmentation +1

A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology

1 code implementation4 Oct 2019 James R. Clough, Nicholas Byrne, Ilkay Oksuz, Veronika A. Zimmer, Julia A. Schnabel, Andrew P. King

We show that the incorporation of the prior knowledge of the topology of this anatomy improves the resulting segmentations in terms of both the topological accuracy and the Dice coefficient.

Anatomy Image Segmentation +2

dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

1 code implementation24 Sep 2019 Jo Schlemper, Ilkay Oksuz, James R. Clough, Jinming Duan, Andrew P. King, Julia A. Schnabel, Joseph V. Hajnal, Daniel Rueckert

AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited.

Topology-preserving augmentation for CNN-based segmentation of congenital heart defects from 3D paediatric CMR

no code implementations23 Aug 2019 Nick Byrne, James R. Clough, Isra Valverde, Giovanni Montana, Andrew P. King

In a series of five-fold cross-validations, we demonstrate the performance gain produced by this pipeline and the relevance of topological considerations to the segmentation of congenital heart defects.

Anatomy Data Augmentation

Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders

no code implementations13 Aug 2019 Esther Puyol-Antón, Bram Ruijsink, James R. Clough, Ilkay Oksuz, Daniel Rueckert, Reza Razavi, Andrew P. King

Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health.

Global and Local Interpretability for Cardiac MRI Classification

no code implementations14 Jun 2019 James R. Clough, Ilkay Oksuz, Esther Puyol-Anton, Bram Ruijsink, Andrew P. King, Julia A. Schnabel

Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their applicability in clinical practice.

Classification General Classification +1

Explicit topological priors for deep-learning based image segmentation using persistent homology

no code implementations29 Jan 2019 James R. Clough, Ilkay Oksuz, Nicholas Byrne, Julia A. Schnabel, Andrew P. King

We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so.

Image Segmentation Left Ventricle Segmentation +2

Magnetic Resonance Fingerprinting using Recurrent Neural Networks

no code implementations19 Dec 2018 Ilkay Oksuz, Gastao Cruz, James Clough, Aurelien Bustin, Nicolo Fuin, Rene M. Botnar, Claudia Prieto, Andrew P. King, Julia A. Schnabel

Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time-efficient acquisition.

Magnetic Resonance Fingerprinting

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

no code implementations29 Oct 2018 Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton, James Clough, Gastao Cruz, Aurelien Bustin, Claudia Prieto, Rene Botnar, Daniel Rueckert, Julia A. Schnabel, Andrew P. King

Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space.

Data Augmentation General Classification +2

Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

no code implementations15 Aug 2018 Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Daniel Rueckert, Julia A. Schnabel, Andrew P. King

As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem.

Data Augmentation Image Quality Assessment +1

Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas

no code implementations27 Jul 2018 Esther Puyol-Anton, Bram Ruijsink, Helene Langet, Mathieu De Craene, Paolo Piro, Julia A. Schnabel, Andrew P. King

The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function.

Association Dimensionality Reduction

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