48 citations · 50 across the 4 of their papers we have counts for
7 papers
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…
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…
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…