58 citations · 173 across the 29 of their papers we have counts for
17 papers · 1 filter
CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR
Wangbin Ding, Lei Li, Junyi Qiu +6
Myocardial infarction (MI) is a leading cause of death worldwide. Late gadolinium enhancement (LGE) and T2-weighted cardiac magnetic resonance (CMR) imaging can respectively identi…
Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion
Guang Yang, Jingkun Chen, Xicheng Sheng +5
Late gadolinium enhancement MRI (LGE MRI) is the gold standard for the detection of myocardial scars for post myocardial infarction (MI). LGE MRI requires the injection of a contra…
Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation
Wangbin Ding, Lei Li, Junyi Qiu +5
Myocardial pathology segmentation (MyoPS) is critical for the risk stratification and treatment planning of myocardial infarction (MI). Multi-sequence cardiac magnetic resonance (M…
Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels
Sihan Wang, Fuping Wu, Lei Li +3
Cardiac segmentation is in great demand for clinical practice. Due to the enormous labor of manual delineation, unsupervised segmentation is desired. The ill-posed optimization pro…
MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images
Junyi Qiu, Lei Li, Sihan Wang +4
Myocardial pathology segmentation (MyoPS) can be a prerequisite for the accurate diagnosis and treatment planning of myocardial infarction. However, achieving this segmentation is…
Multi-Modality Cardiac Image Computing: A Survey
Lei Li, Wangbin Ding, Liqun Huang +2
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and f…
Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation
Zheyao Gao, Lei Li, Fuping Wu +2
Distributed learning has shown great potential in medical image analysis. It allows to use multi-center training data with privacy protection. However, data distributions in local…
AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images
Kai-Ni Wang, Xin Yang, Juzheng Miao +7
Multi-sequence cardiac magnetic resonance (CMR) provides essential pathology information (scar and edema) to diagnose myocardial infarction. However, automatic pathology segmentati…
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Lei Li, Fuping Wu, Sihan Wang +29
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardiu…
Multi-Modality Cardiac Image Analysis with Deep Learning
Lei Li, Fuping Wu, Sihang Wang +1
Accurate cardiac computing, analysis and modeling from multi-modality images are important for the diagnosis and treatment of cardiac disease. Late gadolinium enhancement magnetic…
Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition
Lei Li, Wangbin Ding, Liqun Huang +1
Right ventricular (RV) segmentation from magnetic resonance imaging (MRI) is a crucial step for cardiac morphology and function analysis. However, automatic RV segmentation from MR…
AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs
Lei Li, Veronika A. Zimmer, Julia A. Schnabel +1
Left atrial (LA) segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is a crucial step needed for planning the treatment of atrial fibrillation. However…
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…
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…
AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information
Lei Li, Veronika A. Zimmer, Julia A. Schnabel +1
Left atrial (LA) and atrial scar segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. %, to guide ablation the…
Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention
Lei Li, Xin Weng, Julia A. Schnabel +1
We propose an end-to-end deep neural network (DNN) which can simultaneously segment the left atrial (LA) cavity and quantify LA scars. The framework incorporates the continuous spa…
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
Xiahai Zhuang, Jiahang Xu, Xinzhe Luo +20
Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suff…