15 citations · 38 across the 11 of their papers we have counts for
11 papers
CartiMorph: a framework for automated knee articular cartilage morphometrics
Yongcheng Yao, Junru Zhong, Liping Zhang +2
We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, i…
An Uncertainty Aided Framework for Learning based Liver Mapping and Analysis
Chaoxing Huang, Vincent Wai Sun Wong, Queenie Chan +2
Objective: Quantitative imaging has potential for assessment of biochemical alterations of liver pathologies. Deep learning methods have been employed to accelerate quantita…
CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstruction
Liping Zhang, Xiaobo Li, Weitian Chen
Undersampling k-space data in MRI reduces scan time but pose challenges in image reconstruction. Considerable progress has been made in reconstructing accelerated MRI. However, res…
Uncertainty-weighted Multi-tasking for and T Mapping in the Liver with Self-supervised Learning
Chaoxing Huang, Yurui Qian, Jian Hou +5
Multi-parametric mapping of MRI relaxations in liver has the potential of revealing pathological information of the liver. A self-supervised learning based multi-parametric mapping…
Uncertainty-Aware Self-supervised Neural Network for Liver Mapping with Relaxation Constraint
Chaoxing Huang, Yurui Qian, Simon Chun Ho Yu +6
mapping is a promising quantitative MRI technique for the non-invasive assessment of tissue properties. Learning-based approaches can map from a reduced number of…
Denoising of Three-Dimensional Fast Spin Echo Magnetic Resonance Images of Knee Joints using Spatial-Variant Noise-Relevant Residual Learning of Convolution Neural Network
Shutian Zhao, Donal G. Cahill, Siyue Li +4
Two-dimensional (2D) fast spin echo (FSE) techniques play a central role in the clinical magnetic resonance imaging (MRI) of knee joints. Moreover, three-dimensional (3D) FSE provi…
Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation
Jin Hong, Yu-Dong Zhang, Weitian Chen
Domain adaptation is crucial for transferring the knowledge from the source labeled CT dataset to the target unlabeled MR dataset in abdominal multi-organ segmentation. Meanwhile,…
Multipath CNN with alpha matte inference for knee tissue segmentation from MRI
Sheheryar Khan, Basim Azam, Yongcheng Yao +1
Precise segmentation of knee tissues from magnetic resonance imaging (MRI) is critical in quantitative imaging and diagnosis. Convolutional neural networks (CNNs), which are state…
Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning
Jin Hong, Simon Chun-Ho Yu, Weitian Chen
Liver segmentation on images acquired using computed tomography (CT) and magnetic resonance imaging (MRI) plays an important role in clinical management of liver diseases. Compared…
Bi-phase age-related brain gray matter magnetic resonance T1rho relaxation time change
Yao T Li, Huang Hua, Zhizheng Zhang +3
Objectives: To investigate normative value and age-related change of brain magnetic resonance T1rho relaxation at 1.5 T. Methods: 20 males (age: 40.7+/-15.5 years, range: 22-68 yea…
Improved liver T1rho measurement precision with a breathhold black blood single shot fast spin echo acquisition: a validation study in healthy volunteers
Yi-Xiang Wang, Min Deng, GladsG Lo +3
Purpose: To explore the usability and normal T1rho value of liver parenchyma with a novel single breathhold black blood single shot fast spin echo acquisition based liver imaging s…