407 citations · 1.1k across the 59 of their papers we have counts for
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Personalized 4D Whole Heart Geometry Reconstruction from Cine MRI for Cardiac Digital Twins
Xiaoyue Liu, Xicheng Sheng, Xiahai Zhuang +4
Cardiac digital twins (CDTs) provide personalized in-silico cardiac representations and hold great potential for precision medicine in cardiology. However, whole-heart CDT models t…
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…
CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI
Zi Wang, Fanwen Wang, Chen Qin +29
Cardiac magnetic resonance imaging (MRI) has emerged as a clinically gold-standard technique for diagnosing cardiac diseases, thanks to its ability to provide diverse information w…
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
Jun Lyu, Chen Qin, Shuo Wang +47
Cardiac MRI, crucial for evaluating heart structure and function, faces limitations like slow imaging and motion artifacts. Undersampling reconstruction, especially data-driven alg…
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…
Joint Modeling of Image and Label Statistics for Enhancing Model Generalizability of Medical Image Segmentation
Shangqi Gao, Hangqi Zhou, Yibo Gao +1
Although supervised deep-learning has achieved promising performance in medical image segmentation, many methods cannot generalize well on unseen data, limiting their real-world ap…
Bayesian Image Super-Resolution with Deep Modeling of Image Statistics
Shangqi Gao, Xiahai Zhuang
Modeling statistics of image priors is useful for image super-resolution, but little attention has been paid from the massive works of deep learning-based methods. In this work, we…
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…
CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision
Ke Zhang, Xiahai Zhuang
Curating a large set of fully annotated training data can be costly, especially for the tasks of medical image segmentation. Scribble, a weaker form of annotation, is more obtainab…
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…
Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
Alain Lalande, Zhihao Chen, Thibaut Pommier +30
A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization the…
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 Domain Adaptation with Variational Approximation for Cardiac Segmentation
Fuping Wu, Xiahai Zhuang
Unsupervised domain adaptation is useful in medical image segmentation. Particularly, when ground truths of the target images are not available, domain adaptation can train a targe…
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…
Learning-Based Algorithms for Vessel Tracking: A Review
Dengqiang Jia, Xiahai Zhuang
Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such a…
Rank-One Network: An Effective Framework for Image Restoration
Shangqi Gao, Xiahai Zhuang
The principal rank-one (RO) components of an image represent the self-similarity of the image, which is an important property for image restoration. However, the RO components of a…