870 citations · 1.2k across the 19 of their papers we have counts for
19 papers
Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction
Chen Qin, Jo Schlemper, Kerstin Hammernik +3
We present a deep network interpolation strategy for accelerated parallel MR image reconstruction. In particular, we examine the network interpolation in parameter space between a…
-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction
Kerstin Hammernik, Jo Schlemper, Chen Qin +3
Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a -net, for accelerated…
-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction
Jo Schlemper, Chen Qin, Jinming Duan +2
We explore an ensembled -net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit…
Deep learning for cardiac image segmentation: A review
Chen Chen, Chen Qin, Huaqi Qiu +4
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation pap…
Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction
Jo Schlemper, Jinming Duan, Cheng Ouyang +4
We present simple reconstruction networks for multi-coil data by extending deep cascade of CNN's and exploiting the data consistency layer. In particular, we propose two variants,…
dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
Jo Schlemper, Ilkay Oksuz, James R. Clough +5
AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited. We present dAUTOMAP, a novel way for decomposing the…
Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation
Cheng Ouyang, Konstantinos Kamnitsas, Carlo Biffi +2
Deep learning models trained on medical images from a source domain (e.g. imaging modality) often fail when deployed on images from a different target domain, despite imaging commo…
k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations
Chen Qin, Jo Schlemper, Jinming Duan +4
Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k-space and time. In order to accelerate the dynamic MR imaging and to exploit k-t correlations from highly u…
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…
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…
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…
A new nonlocal forward model for diffuse optical tomography
Wenqi Lu, Jinming Duan, Joshua Deepak Veesa +1
The forward model in diffuse optical tomography (DOT) describes how light propagates through a turbid medium. It is often approximated by a diffusion equation (DE) that is numerica…
Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography
Wenqi Lu, Jinming Duan, David Orive-Miguel +2
Total variation (TV) is a powerful regularization method that has been widely applied in different imaging applications, but is difficult to apply to diffuse optical tomography (DO…
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…
OCT segmentation: Integrating open parametric contour model of the retinal layers and shape constraint to the Mumford-Shah functional
Jinming Duan, Weicheng Xie, Ryan Wen Liu +4
In this paper, we propose a novel retinal layer boundary model for segmentation of optical coherence tomography (OCT) images. The retinal layer boundary model consists of 9 open pa…
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…
Tensor Based Second Order Variational Model for Image Reconstruction
Jinming Duan, Wil OC Ward, Luke Sibbett +2
Second order total variation (SOTV) models have advantages for image reconstruction over their first order counterparts including their ability to remove the staircase artefact in…
Automated Segmentation of Retinal Layers from Optical Coherent Tomography Images Using Geodesic Distance
Jinming Duan, Christopher Tench, Irene Gottlob +2
Optical coherence tomography (OCT) is a non-invasive imaging technique that can produce images of the eye at the microscopic level. OCT image segmentation to localise retinal layer…