765 citations · 1.3k across the 7 of their papers we have counts for
6 papers
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…
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…
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…
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…