no code implementations • 7 Apr 2023 • Gongning Luo, Kuanquan Wang, Jun Liu, Shuo Li, Xinjie Liang, Xiangyu Li, Shaowei Gan, Wei Wang, Suyu Dong, Wenyi Wang, Pengxin Yu, Enyou Liu, Hongrong Wei, Na Wang, Jia Guo, Huiqi Li, Zhao Zhang, Ziwei Zhao, Na Gao, Nan An, Ashkan Pakzad, Bojidar Rangelov, Jiaqi Dou, Song Tian, Zeyu Liu, Yi Wang, Ampatishan Sivalingam, Kumaradevan Punithakumar, Zhaowen Qiu, Xin Gao
Efficient automatic segmentation of multi-level (i. e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications.
no code implementations • 10 Jun 2018 • Suyu Dong, Gongning Luo, Kuanquan Wang, Shaodong Cao, Ashley Mercado, Olga Shmuilovich, Henggui Zhang, Shuo Li
And cGAN advantageously fuses substantial 3D spatial context information from 3D echocardiography by self-learning structured loss; 2) For the first time, it embeds the atlas into an end-to-end optimization framework, which uses 3D LV atlas as a powerful prior knowledge to improve the inference speed, address the lower contrast and the limited annotation problems of 3D echocardiography; 3) It combines traditional discrimination loss and the new proposed consistent constraint, which further improves the generalization of the proposed framework.
1 code implementation • 9 Apr 2018 • Gongning Luo, Suyu Dong, Kuanquan Wang, WangMeng Zuo, Shaodong Cao, Henggui Zhang
Methods: In this paper, we propose a direct volumes prediction method based on the end-to-end deep convolutional neural networks (CNN).