activity
20172021
most citedCombating Uncertainty with Novel Losses for Automatic Left Atrium Segmentation

9 citations · 15 across the 3 of their papers we have counts for

collaborators

5 papers

physics.med-ph2021

: Accelerated Modified Look-Locker Inversion Recovery Myocardial T1 Mapping via Neural Networks

Hossam El-Rewaidy, Rui Guo, Amanda Paskavitz +4

Purpose: To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses neural networks (NN) to estimate voxel-wise myocardial T1 and extracellular (ECV) from…

eess.IV2020

Self-Supervised Physics-Guided Deep Learning Reconstruction For High-Resolution 3D LGE CMR

Burhaneddin Yaman, Chetan Shenoy, Zilin Deng +4

Late gadolinium enhancement (LGE) cardiac MRI (CMR) is the clinical standard for diagnosis of myocardial scar. 3D isotropic LGE CMR provides improved coverage and resolution compar…

cs.CV20189 cited

Combating Uncertainty with Novel Losses for Automatic Left Atrium Segmentation

Xin Yang, Na Wang, Yi Wang +4

Segmenting left atrium in MR volume holds great potentials in promoting the treatment of atrial fibrillation. However, the varying anatomies, artifacts and low contrasts among tiss…

cs.CV20186 cited

Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentation

Cheng Bian, Xin Yang, Jianqiang Ma +5

Accurately segmenting left atrium in MR volume can benefit the ablation procedure of atrial fibrillation. Traditional automated solutions often fail in relieving experts from the l…

cs.RO2017

Offline reconstruction of missing vehicle trajectory data from 3D LIDAR

Cem Sazara, Reza Vatani Nezafat, Mecit Cetin

LIDAR has become an important part of many autonomous vehicles with its advantages on distance measurement and obstacle detection. LIDAR produces point clouds which have important…