10 citations · 30 across the 10 of their papers we have counts for
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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…
Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI
Chen Qin, Shuo Wang, Chen Chen +3
Image registration is an ill-posed inverse problem which often requires regularisation on the solution space. In contrast to most of the current approaches which impose explicit re…
Deep Generative Model-based Quality Control for Cardiac MRI Segmentation
Shuo Wang, Giacomo Tarroni, Chen Qin +7
In recent years, convolutional neural networks have demonstrated promising performance in a variety of medical image segmentation tasks. However, when a trained segmentation model…
Realistic Adversarial Data Augmentation for MR Image Segmentation
Chen Chen, Chen Qin, Huaqi Qiu +6
Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learni…
Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction
Shuo Wang, Chengliang Dai, Yuanhan Mo +3
Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity. Accurate segmentation of gliomas and theirsub-regions on multi-parametric magnetic resonance images…