407 citations · 1.1k across the 59 of their papers we have counts for
60 papers
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…
Toward Universal Medical Image Registration via Sharpness-Aware Meta-Continual Learning
Bomin Wang, Xinzhe Luo, Xiahai Zhuang
Current deep learning approaches in medical image registration usually face the challenges of distribution shift and data collection, hindering real-world deployment. In contrast,…
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…
MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging
Yuanye Liu, Zheyao Gao, Nannan Shi +4
Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a specific sub-region, multi-vie…
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…
Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration
Xinzhe Luo, Xin Wang, Linda Shapiro +3
This article presents a general Bayesian learning framework for multi-modal groupwise image registration. The method builds on probabilistic modelling of the image generative proce…
CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction
Chengyan Wang, Jun Lyu, Shuo Wang +25
Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patien…
A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging
Zheyao Gao, Yuanye Liu, Fuping Wu +3
Staging of liver fibrosis is important in the diagnosis and treatment planning of patients suffering from liver diseases. Current deep learning-based methods using abdominal magnet…
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…
Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior
Kaiwen Wan, Lei Li, Dengqiang Jia +9
Medical images are generally acquired with limited field-of-view (FOV), which could lead to incomplete regions of interest (ROI), and thus impose a great challenge on medical image…
ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation
Ke Zhang, Xiahai Zhuang
Curating a large scale fully-annotated dataset can be both labour-intensive and expertise-demanding, especially for medical images. To alleviate this problem, we propose to utilize…
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…
-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing
Xinzhe Luo, Xiahai Zhuang
This paper presents a generic probabilistic framework for estimating the statistical dependency and finding the anatomical correspondences among an arbitrary number of medical imag…
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…
Deep Compatible Learning for Partially-Supervised Medical Image Segmentation
Ke Zhang, Xiahai Zhuang
Partially-supervised learning can be challenging for segmentation due to the lack of supervision for unlabeled structures, and the methods directly applying fully-supervised learni…
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…
ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation
Ke Zhang, Xiahai Zhuang
Cardiac segmentation is an essential step for the diagnosis of cardiovascular diseases. However, pixel-wise dense labeling is both costly and time-consuming. Scribble, as a form of…
BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement
Xin Wang, Xinzhe Luo, Xiahai Zhuang
Multimodal groupwise registration aligns internal structures in a group of medical images. Current approaches to this problem involve developing similarity measures over the joint…
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…