127 citations · 384 across the 20 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…