38 citations · 88 across the 12 of their papers we have counts for
12 papers
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,…
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…
-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…
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…
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…
A low-rank representation for unsupervised registration of medical images
Dengqiang Jia, Shangqi Gao, Qunlong Chen +2
Registration networks have shown great application potentials in medical image analysis. However, supervised training methods have a great demand for large and high-quality labeled…
Anatomy Prior Based U-net for Pathology Segmentation with Attention
Yuncheng Zhou, Ke Zhang, Xinzhe Luo +2
Pathological area segmentation in cardiac magnetic resonance (MR) images plays a vital role in the clinical diagnosis of cardiovascular diseases. Because of the irregular shape and…
MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation
Xinzhe Luo, Xiahai Zhuang
Current deep-learning-based registration algorithms often exploit intensity-based similarity measures as the loss function, where dense correspondence between a pair of moving and…
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…
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Zhaohan Xiong, Qing Xia, Zhiqiang Hu +41
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial fir…
Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors
Qian Yue, Xinzhe Luo, Qing Ye +2
Cardiac segmentation from late gadolinium enhancement MRI is an important task in clinics to identify and evaluate the infarction of myocardium. The automatic segmentation is howev…
A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images
Jiahang Xu, Fangyang Jiao, Yechong Huang +7
Background: Parkinson's disease (PD) is a prevalent long-term neurodegenerative disease. Though the diagnostic criteria of PD are relatively well defined, the current medical imagi…