50 citations · 125 across the 14 of their papers we have counts for
14 papers
Structure Guided Manifolds for Discovery of Disease Characteristics
Siyu Liu, Linfeng Liu, Xuan Vinh +4
In medical image analysis, the subtle visual characteristics of many diseases are challenging to discern, particularly due to the lack of paired data. For example, in mild Alzheime…
BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources
Xuanyu Zhu, Yang Gao, Feng Liu +2
Introduction: Background field removal (BFR) is a critical step required for successful quantitative susceptibility mapping (QSM). However, eliminating the background field in brai…
Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks
Yang Gao, Zhuang Xiong, Amir Fazlollahi +8
Quantitative susceptibility mapping (QSM) is a valuable MRI post-processing technique that quantifies the magnetic susceptibility of body tissue from phase data. However, the tradi…
CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation
Wei Dai, Boyeong Woo, Siyu Liu +6
Direct automatic segmentation of objects from 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying a number of in…
Bespoke Fractal Sampling Patterns for Discrete Fourier Space via the Kaleidoscope Transform
Jacob M. White, Stuart Crozier, Shekhar S. Chandra
Sampling strategies are important for sparse imaging methodologies, especially those employing the discrete Fourier transform (DFT). Chaotic sensing is one such methodology that em…
Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning
Xuanyu Zhu, Yang Gao, Feng Liu +2
Introduction: Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM)…
Deep Simultaneous Optimisation of Sampling and Reconstruction for Multi-contrast MRI
Xinwen Liu, Jing Wang, Fangfang Tang +3
MRI images of the same subject in different contrasts contain shared information, such as the anatomical structure. Utilizing the redundant information amongst the contrasts to sub…
Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network
Yang Gao, Martijn Cloos, Feng Liu +3
Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing method that quantifies tissue magnetic susceptibility distributions. However, QSM acquisitions are r…
Manipulating Medical Image Translation with Manifold Disentanglement
Siyu Liu, Jason A. Dowling, Craig Engstrom +3
Medical image translation (e.g. CT to MR) is a challenging task as it requires I) faithful translation of domain-invariant features (e.g. shape information of anatomical structures…
Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI
Hang Min, Darryl McClymont, Shekhar S. Chandra +2
Previous studies on computer aided detection/diagnosis (CAD) in 4D breast magnetic resonance imaging (MRI) regard lesion detection, segmentation and characterization as separate ta…
Generalisable 3D Fabric Architecture for Streamlined Universal Multi-Dataset Medical Image Segmentation
Siyu Liu, Wei Dai, Craig Engstrom +4
Data scarcity is common in deep learning models for medical image segmentation. Previous works proposed multi-dataset learning, either simultaneously or via transfer learning to ex…
xQSM: Quantitative Susceptibility Mapping with Octave Convolutional and Noise Regularized Neural Networks
Yang Gao, Xuanyu Zhu, Bradford A. Moffat +6
Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications. However, the image re…
Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN
Hang Min, Devin Wilson, Yinhuang Huang +4
Mammographic mass detection and segmentation are usually performed as serial and separate tasks, with segmentation often only performed on manually confirmed true positive detectio…
Minimax Current Density Coil Design
Michael Poole, Pierre Weiss, Hector Sanchez Lopez +2
'Coil design' is an inverse problem in which arrangements of wire are designed to generate a prescribed magnetic field when energized with electric current. The design of gradient…