765 citations · 1.2k across the 23 of their papers we have counts for
23 papers
TIP: Tabular-Image Pre-training for Multimodal Classification with Incomplete Data
Siyi Du, Shaoming Zheng, Yinsong Wang +3
Images and structured tables are essential parts of real-world databases. Though tabular-image representation learning is promising to create new insights, it remains a challenging…
DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning
Qingjie Meng, Wenjia Bai, Declan P O'Regan +1
3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current sta…
CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy
Mengyun Qiao, Shuo Wang, Huaqi Qiu +4
Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical facto…
Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta
Simone Saitta, Ludovica Maga, Chloe Armour +8
Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoracic aortic aneurysms (ATAA). To accurately reproduce hemodynamics, comp…
Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learning
Qingjie Meng, Wenjia Bai, Tianrui Liu +2
3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and diagnosis of cardiovascular diseases. Most of the pre…
MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI
Qingjie Meng, Chen Qin, Wenjia Bai +4
Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding a…
Correspondence on ACMG STATEMENT: ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG) by Miller et al
Kathryn A. McGurk, Sean L. Zheng, Albert Henry +8
We were interested to read the recent update on recommendations for reporting of secondary findings in clinical sequencing1, and the accompanying updated list of genes in which sec…
Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration
Alexander Thorley, Xi Jia, Hyung Jin Chang +8
Deterministic approaches using iterative optimisation have been historically successful in diffeomorphic image registration (DiffIR). Although these approaches are highly accurate,…
Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation
Nicolo Savioli, Antonio de Marvao, Wenjia Bai +5
Optimising the analysis of cardiac structure and function requires accurate 3D representations of shape and motion. However, techniques such as cardiac magnetic resonance imaging a…
Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation
Shuo Wang, Chen Qin, Nicolo Savioli +6
In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the…
Learning a Model-Driven Variational Network for Deformable Image Registration
Xi Jia, Alexander Thorley, Wei Chen +9
Data-driven deep learning approaches to image registration can be less accurate than conventional iterative approaches, especially when training data is limited. To address this wh…
Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network
Shihao Jin, Nicolò Savioli, Antonio de Marvao +4
In this work, a novel approach is proposed for joint analysis of high dimensional time-resolved cardiac motion features obtained from segmented cardiac MRI and low dimensional clin…
VS-Net: Variable splitting network for accelerated parallel MRI reconstruction
Jinming Duan, Jo Schlemper, Chen Qin +7
In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high…
Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models
Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni +12
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of p…
3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders
Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni +4
Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imag…
Deep learning cardiac motion analysis for human survival prediction
Ghalib A. Bello, Timothy J. W. Dawes, Jinming Duan +8
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems require…
A Comprehensive Approach for Learning-based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks
Giacomo Tarroni, Ozan Oktay, Matthew Sinclair +7
In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks are acquired during multiple subsequent breath-holds. If the patient cannot consistently hold the breath…
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
Jinming Duan, Ghalib Bello, Jo Schlemper +7
Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image…
Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension
Jinming Duan, Jo Schlemper, Wenjia Bai +6
In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH). The proposed method ex…
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
Carlo Biffi, Ozan Oktay, Giacomo Tarroni +9
Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular disea…
Learning-Based Quality Control for Cardiac MR Images
Giacomo Tarroni, Ozan Oktay, Wenjia Bai +9
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned…
Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework
Carlo Biffi, Antonio de Marvao, Mark I. Attard +10
MOTIVATION: Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping rel…
Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation
Ozan Oktay, Enzo Ferrante, Konstantinos Kamnitsas +10
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where image…