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20192024
most cited-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing

38 citations · 88 across the 12 of their papers we have counts for

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7 papers · 1 filter

cs.CV2024

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,…

cs.CV2024★ 1 cited

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…

cs.CV2022★ 38 cited

-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…

cs.CV2022

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…

cs.CV2021★ 3 cited

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…

cs.CV2020★ 1 cited

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…

cs.CV2020★ 13 cited

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…