17 Result(s)
within
Conference Paper
Jo Schlemper
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Chapter and Conference Paper
ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration
Establishing voxelwise semantic correspondence across distinct imaging modalities is a foundational yet formidable computer vision task. Current multi-modality registration techniques maximize hand-crafted int...
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Chapter and Conference Paper
Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge
The 2019 fastMRI challenge was an open challenge designed to advance research in the field of machine learning for MR image reconstruction. The goal for the participants was to reconstruct undersampled MRI k-spac...
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Chapter and Conference Paper
Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training
Deep learning based registration methods have emerged as alternatives to traditional registration methods, with competitive accuracy and significantly less runtime. Two different strategies have been proposed ...
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Chapter and Conference Paper
Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation
In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomic...
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Chapter and Conference Paper
Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction
Deep learning for accelerated magnetic resonance (MR) image reconstruction is a fast growing field, which has so far shown promising results. However, most works are limited in the sense that they assume equi...
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Chapter and Conference Paper
k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-Temporal Correlations
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 undersampled data, here we propos...
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Chapter and Conference Paper
Exploiting Motion for Deep Learning Reconstruction of Extremely-Undersampled Dynamic MRI
The problem of accelerated acquisition for dynamic MRI has been recently tackled with deep learning techniques. However, current state-of-the-art approaches do not incorporate a strategy to exploit the full t...
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Chapter and Conference Paper
VS-Net: Variable Splitting Network for Accelerated Parallel MRI Reconstruction
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-quality reconstruction of un...
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Chapter and Conference Paper
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE) loss functions can achieve hi...
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Chapter and Conference Paper
Bayesian Deep Learning for Accelerated MR Image Reconstruction
Recently, many deep learning (DL) based MR image reconstruction methods have been proposed with promising results. However, only a handful of work has been focussing on characterising the behaviour of deep networ...
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Chapter and Conference Paper
Combining Deep Learning and Shape Priors for Bi-Ventricular Segmentation of Volumetric Cardiac Magnetic Resonance Images
In this paper, we combine a network-based method with image registration to develop a shape-based bi-ventricular segmentation tool for short-axis cardiac magnetic resonance (CMR) volumetric images. The method ...
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Chapter and Conference Paper
Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension
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 explicitly takes into account t...
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Chapter and Conference Paper
Cardiac MR Segmentation from Undersampled k-space Using Deep Latent Representation Learning
Reconstructing magnetic resonance imaging (MRI) from undersampled k-space enables the accelerated acquisition of MRI but is a challenging problem. However, in many diagnostic scenarios, perfect reconstructions ar...
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Chapter and Conference Paper
Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image
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. However, if the main purpose is t...
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Chapter and Conference Paper
Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI
Understanding the structure of the heart at the microscopic scale of cardiomyocytes and their aggregates provides new insights into the mechanisms of heart disease and enables the investigation of effective th...
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Chapter and Conference Paper
Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences
Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel deep learning method for j...
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Chapter and Conference Paper
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cas...
