Search Results for author:
Found 15 papers, 7 papers with code
Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN
We propose a fully-integrated computer-aided detection (CAD) system for simultaneous mammographic mass detection and segmentation without user intervention.
xQSM-Quantitative Susceptibility Mapping with Octave Convolutional Neural Networks
Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications.
Image and Video Processing
Generalisable 3D Fabric Architecture for Streamlined Universal Multi-Dataset Medical Image Segmentation
However, medical image datasets have diverse-sized images and features, and developing a model simultaneously for multiple datasets is challenging.
Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI
Breast lesions are firstly extracted as region candidates using the novel 3D multiscale morphological sifting (MMS).
Manipulating Medical Image Translation with Manifold Disentanglement
In this work, we propose Manifold Disentanglement Generative Adversarial Network (MDGAN), a novel image translation framework that explicitly models these two types of features.
Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network
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.
Deep Simultaneous Optimisation of Sampling and Reconstruction for Multi-contrast MRI
MRI images of the same subject in different contrasts contain shared information, such as the anatomical structure.
Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning
Method: A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages.
Bespoke Fractal Sampling Patterns for Discrete Fourier Space via the Kaleidoscope Transform
Sampling strategies are important for sparse imaging methodologies, especially those employing the discrete Fourier transform (DFT).
CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation
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.
Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks
In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the novel neural networks.
Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from 3D magnetic resonance images
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.
BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources
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.
Structure Guided Manifolds for Discovery of Disease Characteristics
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).
Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative
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.
