127 citations · 384 across the 20 of their papers we have counts for
20 papers
An AI tool for automated analysis of large-scale unstructured clinical cine CMR databases
Jorge Mariscal-Harana, Clint Asher, Vittoria Vergani +9
Artificial intelligence (AI) techniques have been proposed for automating analysis of short axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR analysis tool exists to aut…
Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation
Esther Puyol-Anton, Bram Ruijsink, Stefan K. Piechnik +4
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…
Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study
Matthew Ng, Fumin Guo, Labonny Biswas +4
Objective: Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world…
A radiomics approach to analyze cardiac alterations in hypertension
Irem Cetin, Steffen E. Petersen, Sandy Napel +3
Hypertension is a medical condition that is well-established as a risk factor for many major diseases. For example, it can cause alterations in the cardiac structure and function o…
Fully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobank
Edward Ferdian, Avan Suinesiaputra, Kenneth Fung +9
Purpose: To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac magnetic resonance tagged…
A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI
Irem Cetin, Gerard Sanroma, Steffen E. Petersen +4
Use expert visualization or conventional clinical indices can lack accuracy for borderline classications. Advanced statistical approaches based on eigen-decomposition have been mos…
Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI
Víctor M. Campello, Carlos Martín-Isla, Cristian Izquierdo +3
Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue.…
Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image
Chen Qin, Wenjia Bai, Jo Schlemper +4
Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. H…
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
Wenjia Bai, Chen Chen, Giacomo Tarroni +6
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety o…
Improving the generalizability of convolutional neural network-based segmentation on CMR images
Chen Chen, Wenjia Bai, Rhodri H. Davies +13
Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR…
3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata
Rahman Attar, Marco Pereanez, Christopher Bowles +4
Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approac…
Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images
Chen Chen, Carlo Biffi, Giacomo Tarroni +3
Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and func…
Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank
Qiao Zheng, Hervé Delingette, Kenneth Fung +2
We perform unsupervised analysis of image-derived shape and motion features extracted from 3822 cardiac 4D MRIs of the UK Biobank. First, with a feature extraction method previousl…
Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study
Robert Robinson, Vanya V. Valindria, Wenjia Bai +19
Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic proc…
High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort
Rahman Attar, Marco Pereanez, Ali Gooya +6
The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throug…
Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN
Le Zhang, Ali Gooya, Marco Pereanez +5
Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic imaging of cardiovascular diseases. Full coverage of the left ventricle (LV), from base to apex, is a…
Real-time Prediction of Segmentation Quality
Robert Robinson, Ozan Oktay, Wenjia Bai +17
Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due…
Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences
Chen Qin, Wenjia Bai, Jo Schlemper +4
Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a nov…
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
Wenjia Bai, Matthew Sinclair, Giacomo Tarroni +20
Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accura…
Clinical Text Prediction with Numerically Grounded Conditional Language Models
Georgios P. Spithourakis, Steffen E. Petersen, Sebastian Riedel
Assisted text input techniques can save time and effort and improve text quality. In this paper, we investigate how grounded and conditional extensions to standard neural language…