3 citations · 6 across the 5 of their papers we have counts for
5 papers
MGML: A Plug-and-Play Meta-Guided Multi-Modal Learning Framework for Incomplete Multimodal Brain Tumor Segmentation
Yulong Zou, Bo Liu, Cun-Jing Zheng +6
Leveraging multimodal information from Magnetic Resonance Imaging (MRI) plays a vital role in lesion segmentation, especially for brain tumors. However, in clinical practice, multi…
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…
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation
Bo Liu, Yudong Zhang, Shuihua Wang +2
Retinal vascular morphology is crucial for diagnosing diseases such as diabetes, glaucoma, and hypertension, making accurate segmentation of retinal vessels essential for early int…
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…