most citedMotion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation

5 citations · 10 across the 4 of their papers we have counts for

collaborators

5 papers

eess.IV20213 cited

Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels

Yuqian Zhou, Hanchao Yu, Humphrey Shi

Retinal vessel segmentation from retinal images is an essential task for developing the computer-aided diagnosis system for retinal diseases. Efforts have been made on high-perform…

eess.IV2020

Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks

Qiaoying Huang, Eric Z. Chen, Hanchao Yu +4

Accurate estimation of shape thickness from medical images is crucial in clinical applications. For example, the thickness of myocardium is one of the key to cardiac disease diagno…

eess.IV20202 cited

Anatomy-Aware Cardiac Motion Estimation

Pingjun Chen, Xiao Chen, Eric Z. Chen +3

Cardiac motion estimation is critical to the assessment of cardiac function. Myocardium feature tracking (FT) can directly estimate cardiac motion from cine MRI, which requires no…

eess.IV20205 cited

Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation

Hanchao Yu, Xiao Chen, Humphrey Shi +3

Cardiac motion estimation plays a key role in MRI cardiac feature tracking and function assessment such as myocardium strain. In this paper, we propose Motion Pyramid Networks, a n…

cs.CV2020

FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation

Hanchao Yu, Shanhui Sun, Haichao Yu +4

Motion estimation of cardiac MRI videos is crucial for the evaluation of human heart anatomy and function. Recent researches show promising results with deep learning-based methods…