15 citations · 29 across the 5 of their papers we have counts for
5 papers
Utilizing 3D Fast Spin Echo Anatomical Imaging to Reduce the Number of Contrast Preparations in Quantification of Knee Cartilage Using Learning-Based Methods
Junru Zhong, Chaoxing Huang, Ziqiang Yu +7
Purpose: To propose and evaluate an accelerated quantification method that combines -weighted fast spin echo (FSE) images and proton density (PD)-weighted anatomic…
ERANet: Edge Replacement Augmentation for Semi-Supervised Meniscus Segmentation with Prototype Consistency Alignment and Conditional Self-Training
Siyue Li, Yongcheng Yao, Junru Zhong +9
Manual segmentation is labor-intensive, and automatic segmentation remains challenging due to the inherent variability in meniscal morphology, partial volume effects, and low contr…
A Systematic Post-Processing Approach for Quantitative Imaging of Knee Articular Cartilage
Junru Zhong, Yongcheng Yao, Fan Xiao +7
Objective: To establish an automated pipeline for post-processing of quantitative spin-lattice relaxation time constant in the rotating frame () imaging of knee articular c…
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…
Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification
Junru Zhong, Yongcheng Yao, Donal G. Cahill +7
Purpose: The aim of this study was to demonstrate the utility of unsupervised domain adaptation (UDA) in automated knee osteoarthritis (OA) phenotype classification using a small d…