Alert button
Picture for Ning Huang

Ning Huang

Alert button

Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images

Jul 03, 2021
Hejie Cui, Xinglong Liu, Ning Huang

Figure 1 for Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images
Figure 2 for Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images
Figure 3 for Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images
Figure 4 for Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images
Viaarxiv icon

Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks

Jul 07, 2020
Guotai Wang, Tao Song, Qiang Dong, Mei Cui, Ning Huang, Shaoting Zhang

Figure 1 for Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks
Figure 2 for Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks
Figure 3 for Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks
Figure 4 for Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks
Viaarxiv icon

Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation

May 27, 2020
Jingyang Zhang, Guotai Wang, Hongzhi Xie, Shuyang Zhang, Ning Huang, Shaoting Zhang, Lixu Gu

Figure 1 for Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation
Figure 2 for Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation
Figure 3 for Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation
Figure 4 for Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation
Viaarxiv icon

A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging

May 07, 2020
Zhaohan Xiong, Qing Xia, Zhiqiang Hu, Ning Huang, Cheng Bian, Yefeng Zheng, Sulaiman Vesal, Nishant Ravikumar, Andreas Maier, Xin Yang, Pheng-Ann Heng, Dong Ni, Caizi Li, Qianqian Tong, Weixin Si, Elodie Puybareau, Younes Khoudli, Thierry Geraud, Chen Chen, Wenjia Bai, Daniel Rueckert, Lingchao Xu, Xiahai Zhuang, Xinzhe Luo, Shuman Jia, Maxime Sermesant, Yashu Liu, Kuanquan Wang, Davide Borra, Alessandro Masci, Cristiana Corsi, Coen de Vente, Mitko Veta, Rashed Karim, Chandrakanth Jayachandran Preetha, Sandy Engelhardt, Menyun Qiao, Yuanyuan Wang, Qian Tao, Marta Nunez-Garcia, Oscar Camara, Nicolo Savioli, Pablo Lamata, Jichao Zhao

Figure 1 for A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Figure 2 for A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Figure 3 for A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Figure 4 for A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Viaarxiv icon