Search Results for author: Stuart Crozier

Found 15 papers, 7 papers with code

Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative

1 code implementation30 Nov 2022 Boyeong Woo, Craig Engstrom, William Baresic, Jurgen Fripp, Stuart Crozier, Shekhar S. Chandra

A second anomaly-aware network, which was compared to anomaly-na\"ive segmentation networks, was used to provide a final automated segmentation of the femoral, tibial and patellar bones and cartilages from the knee MR images containing a spectrum of bone anomalies.

Anomaly Detection Segmentation +2

Structure Guided Manifolds for Discovery of Disease Characteristics

no code implementations22 Sep 2022 Siyu Liu, Linfeng Liu, Xuan Vinh, Stuart Crozier, Craig Engstrom, Fatima Nasrallah, Shekhar Chandra

DiDiGAN learns a disease manifold of AD and CN visual characteristics, and the style codes sampled from this manifold are imposed onto an anatomical structural "blueprint" to synthesise paired AD and CN magnetic resonance images (MRIs).

BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources

1 code implementation6 Apr 2022 Xuanyu Zhu, Yang Gao, Feng Liu, Stuart Crozier, Hongfu Sun

The BFRnet method is compared with three conventional BFR methods and one previous deep learning method using simulated and in vivo brains from 4 healthy and 2 hemorrhagic subjects.

Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from 3D magnetic resonance images

no code implementations6 Dec 2021 Jessica M. Bugeja, Ying Xia, Shekhar S. Chandra, Nicholas J. Murphy, Jillian Eyles, Libby Spiers, Stuart Crozier, David J. Hunter, Jurgen Fripp, Craig Engstrom

Automated analyses of 3D MR images from patients with FAI using the CamMorph pipeline showed that, in comparison with female patients, male patients had significantly greater cam volume, surface area and height.


Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks

2 code implementations15 Nov 2021 Yang Gao, Zhuang Xiong, Amir Fazlollahi, Peter J Nestor, Viktor Vegh, Fatima Nasrallah, Craig Winter, G. Bruce Pike, Stuart Crozier, Feng Liu, Hongfu Sun

In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the novel neural networks.

CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation

no code implementations12 Sep 2021 Wei Dai, Boyeong Woo, Siyu Liu, Matthew Marques, Craig B. Engstrom, Peter B. Greer, Stuart Crozier, Jason A. Dowling, Shekhar S. Chandra

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 individual objects with complex geometries within a large volume under investigation.

Image Segmentation Medical Image Segmentation +1

Bespoke Fractal Sampling Patterns for Discrete Fourier Space via the Kaleidoscope Transform

no code implementations2 Aug 2021 Jacob M. White, Stuart Crozier, Shekhar S. Chandra

Sampling strategies are important for sparse imaging methodologies, especially those employing the discrete Fourier transform (DFT).

Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning

no code implementations1 Jun 2021 Xuanyu Zhu, Yang Gao, Feng Liu, Stuart Crozier, Hongfu Sun

Method: A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages.

Deep Simultaneous Optimisation of Sampling and Reconstruction for Multi-contrast MRI

no code implementations31 Mar 2021 Xinwen Liu, Jing Wang, Fangfang Tang, Shekhar S. Chandra, Feng Liu, Stuart Crozier

MRI images of the same subject in different contrasts contain shared information, such as the anatomical structure.


Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network

1 code implementation17 Mar 2021 Yang Gao, Martijn Cloos, Feng Liu, Stuart Crozier, G. Bruce Pike, Hongfu Sun

In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM acquisition.


Manipulating Medical Image Translation with Manifold Disentanglement

no code implementations27 Nov 2020 Siyu Liu, Jason A. Dowling, Craig Engstrom, Peter B. Greer, Stuart Crozier, Shekhar S. Chandra

In this work, we propose Manifold Disentanglement Generative Adversarial Network (MDGAN), a novel image translation framework that explicitly models these two types of features.

Disentanglement Generative Adversarial Network +1

Generalisable 3D Fabric Architecture for Streamlined Universal Multi-Dataset Medical Image Segmentation

1 code implementation28 Jun 2020 Siyu Liu, Wei Dai, Craig Engstrom, Jurgen Fripp, Stuart Crozier, Jason A. Dowling, Shekhar S. Chandra

However, medical image datasets have diverse-sized images and features, and developing a model simultaneously for multiple datasets is challenging.

Anatomy Image Segmentation +4

xQSM-Quantitative Susceptibility Mapping with Octave Convolutional Neural Networks

1 code implementation14 Apr 2020 Yang Gao, Xuanyu Zhu, Stuart Crozier, Feng Liu, Hongfu Sun

Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications.

Image and Video Processing

Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN

1 code implementation28 Jun 2019 Hang Min, Devin Wilson, Yinhuang Huang, Siyu Liu, Stuart Crozier, Andrew P. Bradley, Shekhar S. Chandra

We propose a fully-integrated computer-aided detection (CAD) system for simultaneous mammographic mass detection and segmentation without user intervention.

Image Generation Segmentation +1

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