407 citations · 1.1k across the 59 of their papers we have counts for
28 papers · 1 filter
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,…
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…
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…
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…
-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…
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…
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…
Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review
Lei Li, Veronika A. Zimmer, Julia A. Schnabel +1
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of scars provide importa…
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…
VSpSR: Explorable Super-Resolution via Variational Sparse Representation
Hangqi Zhou, Chao Huang, Shangqi Gao +1
Super-resolution (SR) is an ill-posed problem, which means that infinitely many high-resolution (HR) images can be degraded to the same low-resolution (LR) image. To study the one-…
Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information
Lei Li, Veronika A. Zimmer, Wangbin Ding +4
Deep learning (DL)-based models have demonstrated good performance in medical image segmentation. However, the models trained on a known dataset often fail when performed on an uns…
Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks
Wangbin Ding, Lei Li, Xiahai Zhuang +1
Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the intensity similarity between target and atlas images. However, such similarity can be pro…
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…
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…
KLDivNet: An unsupervised neural network for multi-modality image registration
Yechong Huang, Tao Song, Jiahang Xu +2
Multi-modality image registration is one of the most underlined processes in medical image analysis. Recently, convolutional neural networks (CNNs) have shown significant potential…
Multi-scale deep neural networks for real image super-resolution
Shangqi Gao, Xiahai Zhuang
Single image super-resolution (SR) is extremely difficult if the upscaling factors of image pairs are unknown and different from each other, which is common in real image SR. To ta…
Diagnosis of Alzheimer's Disease via Multi-modality 3D Convolutional Neural Network
Yechong Huang, Jiahang Xu, Yuncheng Zhou +3
Alzheimer's Disease (AD) is one of the most concerned neurodegenerative diseases. In the last decade, studies on AD diagnosis attached great significance to artificial intelligence…
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
Xiahai Zhuang, Lei Li, Christian Payer +31
Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable…
Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework
Lei Li, Fuping Wu, Guang Yang +6
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the…
PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation
Qi Dou, Cheng Ouyang, Cheng Chen +4
Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks. Leveraging images from different modalities for the same an…