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20182023
most citedDeepReg: a deep learning toolkit for medical image registration

47 citations · 122 across the 16 of their papers we have counts for

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Showing eess.IVShow all

8 papers · 1 filter

eess.IV2023★ 42 cited

Low-field magnetic resonance image enhancement via stochastic image quality transfer

Hongxiang Lin, Matteo Figini, Felice D'Arco +10

Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher…

eess.IV2022

Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine Learning

Jason P. Lim, Stefano B. Blumberg, Neil Narayan +4

Machine learning is a powerful approach for fitting microstructural models to diffusion MRI data. Early machine learning microstructure imaging implementations trained regressors t…

eess.IV2022

VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation

Yukun Zhou, Moucheng Xu, Yipeng Hu +5

Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for bo…

eess.IV2022★ 3 cited

Progressive Subsampling for Oversampled Data - Application to Quantitative MRI

Stefano B. Blumberg, Hongxiang Lin, Francesco Grussu +3

We present PROSUB: PROgressive SUBsampling, a deep learning based, automated methodology that subsamples an oversampled data set (e.g. multi-channeled 3D images) with minimal loss…

eess.IV2020★ 47 cited

DeepReg: a deep learning toolkit for medical image registration

Yunguan Fu, Nina Montaña Brown, Shaheer U. Saeed +14

DeepReg (https://github.com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.

eess.IV2020★ 4 cited

Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients

Matteo Figini, Hongxiang Lin, Godwin Ogbole +10

1.5T or 3T scanners are the current standard for clinical MRI, but low-field (<1T) scanners are still common in many lower- and middle-income countries for reasons of cost and robu…

eess.IV2019

Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia

Daniele Ravi, Stefano B. Blumberg, Silvia Ingala +3

Accurate and realistic simulation of high-dimensional medical images has become an important research area relevant to many AI-enabled healthcare applications. However, current sta…

eess.IV2019★ 2 cited

Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator

Hongxiang Lin, Matteo Figini, Ryutaro Tanno +10

MR images scanned at low magnetic field (T) have lower resolution in the slice direction and lower contrast, due to a relatively small signal-to-noise ratio (SNR) than those fr…