activity
20202022
most citedBrain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint

3 citations · 8 across the 6 of their papers we have counts for

collaborators

8 papers

eess.IV20221 cited

Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion

Wangbin Ding, Lei Li, Xiahai Zhuang +1

Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding la…

eess.IV2021

Unsupervised Multi-Modality Registration Network based on Spatially Encoded Gradient Information

Wangbin Ding, Lei Li, Xiahai Zhuang +1

Multi-modality medical images can provide relevant or complementary information for a target (organ, tumor or tissue). Registering multi-modality images to a common space can fuse…

eess.IV2021

Automatic Pulmonary Artery-Vein Separation in CT Images using Twin-Pipe Network and Topology Reconstruction

Lin Pan, Yaoyong Zheng, Liqin Huang +5

With the development of medical computer-aided diagnostic systems, pulmonary artery-vein(A/V) separation plays a crucial role in assisting doctors in preoperative planning for lung…

eess.IV20211 cited

Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growing

Jinquan Guo, Rongda Fu, Lin Pan +4

Automatic airway segmentation from chest computed tomography (CT) scans plays an important role in pulmonary disease diagnosis and computer-assisted therapy. However, low contrast…

eess.IV20203 cited

Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint

Chenyu Liu, Wangbin Ding, Lei Li +4

Delineating the brain tumor from magnetic resonance (MR) images is critical for the treatment of gliomas. However, automatic delineation is challenging due to the complex appearanc…

cs.CV20202 cited

Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information

Lei Li, Veronika A. Zimmer, Wangbin Ding +4

Deep learning (DL)-based models have demonstrated good performance in medical image segmentation. However, the models trained on a known dataset often fail when performed on an uns…