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20182021
most citedShape-Aware Organ Segmentation by Predicting Signed Distance Maps

13 citations · 27 across the 5 of their papers we have counts for

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cs.CV20214 cited

Test-Time Training for Deformable Multi-Scale Image Registration

Wentao Zhu, Yufang Huang, Daguang Xu +3

Registration is a fundamental task in medical robotics and is often a crucial step for many downstream tasks such as motion analysis, intra-operative tracking and image segmentatio…

cs.CV2020

Partly Supervised Multitask Learning

Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang +5

Semi-supervised learning has recently been attracting attention as an alternative to fully supervised models that require large pools of labeled data. Moreover, optimizing a model…

cs.CV201913 cited

Shape-Aware Organ Segmentation by Predicting Signed Distance Maps

Yuan Xue, Hui Tang, Zhi Qiao +6

In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall sha…

cs.CV20195 cited

Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking

Wentao Zhu, Yufang Huang, Mani A Vannan +5

Echocardiography has become routinely used in the diagnosis of cardiomyopathy and abnormal cardiac blood flow. However, manually measuring myocardial motion and cardiac blood flow…

cs.CV2018

AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy

Wentao Zhu, Yufang Huang, Liang Zeng +6

Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generati…