Search Results for author: Xiahai Zhuang

Found 53 papers, 14 papers with code

A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging

no code implementations21 Jun 2023 Zheyao Gao, Yuanye Liu, Fuping Wu, Nannan Shi, Yuxin Shi, Xiahai Zhuang

Therefore, we propose a reliable multi-view learning method with interpretable combination rules, which can model global representations to improve the accuracy of predictions.


BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability

no code implementations3 Mar 2023 Shangqi Gao, Hangqi Zhou, Yibo Gao, Xiahai Zhuang

Specifically, we first decompose an image into a spatial-correlated variable and a spatial-variant variable, assigning hierarchical Bayesian priors to explicitly force them to model the domain-stable shape and domain-specific appearance information respectively.

Cardiac Segmentation Domain Generalization +2

Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation

no code implementations7 Feb 2023 Wangbin Ding, Lei LI, Junyi Qiu, Sihan Wang, Liqin Huang, Yinyin Chen, Shan Yang, Xiahai Zhuang

For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively.

Image Registration

Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior

no code implementations13 Jan 2023 Kaiwen Wan, Lei LI, Dengqiang Jia, Shangqi Gao, Wei Qian, Yingzhi Wu, Huandong Lin, Xiongzheng Mu, Xin Gao, Sijia Wang, Fuping Wu, Xiahai Zhuang

This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets.

Reinforcement Learning (RL)

ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation

no code implementations12 Jan 2023 Ke Zhang, Xiahai Zhuang

Curating a large scale fully-annotated dataset can be both labour-intensive and expertise-demanding, especially for medical images.

Image Segmentation Medical Image Segmentation +2

MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images

no code implementations6 Nov 2022 Junyi Qiu, Lei LI, Sihan Wang, Ke Zhang, Yinyin Chen, Shan Yang, Xiahai Zhuang

We therefore conducted extensive experiments to investigate the performance of the proposed method in dealing with such complex combinations of different CMR sequences.

$\mathcal{X}$-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing

no code implementations3 Nov 2022 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 images.


Multi-Modality Cardiac Image Computing: A Survey

no code implementations26 Aug 2022 Lei LI, Wangbin Ding, Liqun Huang, Xiahai Zhuang, Vicente Grau

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases.


Deep Compatible Learning for Partially-Supervised Medical Image Segmentation

no code implementations18 Jun 2022 Ke Zhang, Xiahai Zhuang

To address the challenge, we propose a deep compatible learning (DCL) framework, which trains a single multi-label segmentation network using images with only partial structures annotated.

Image Segmentation Medical Image Segmentation +1

Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation

1 code implementation10 Jun 2022 Zheyao Gao, Lei LI, Fuping Wu, Sihan Wang, Xiahai Zhuang

In this work, we propose a new framework of distributed learning that bridges the gap between two groups, and improves the performance for both generic and local data.

MRI segmentation

Joint Modeling of Image and Label Statistics for Enhancing Model Generalizability of Medical Image Segmentation

no code implementations9 Jun 2022 Shangqi Gao, Hangqi Zhou, Yibo Gao, Xiahai Zhuang

To address this problem, we propose a deep learning-based Bayesian framework, which jointly models image and label statistics, utilizing the domain-irrelevant contour of a medical image for segmentation.

Image Segmentation MRI segmentation +1

ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation

1 code implementation5 Jun 2022 Ke Zhang, Xiahai Zhuang

To tackle this problem, we propose a new scribble-guided method for cardiac segmentation, based on the Positive-Unlabeled (PU) learning framework and global consistency regularization, and termed as ShapePU.

Cardiac Segmentation

Bayesian Image Super-Resolution with Deep Modeling of Image Statistics

1 code implementation31 Mar 2022 Shangqi Gao, Xiahai Zhuang

In this work, we propose a Bayesian image restoration framework, where natural image statistics are modeled with the combination of smoothness and sparsity priors.

Image Restoration Image Super-Resolution

CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision

1 code implementation CVPR 2022 Ke Zhang, Xiahai Zhuang

To address the difficulties, we propose a new framework for scribble learning-based medical image segmentation, which is composed of mix augmentation and cycle consistency and thus is referred to as CycleMix.

Image Segmentation Medical Image Segmentation +1

Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion

1 code implementation4 Feb 2022 Wangbin Ding, Lei LI, Xiahai Zhuang, Liqin Huang

For the label fusion, we design a similarity estimation network (SimNet), which estimates the fusion weight of each atlas by measuring its similarity to the target image.

Image Registration Image Segmentation +2

AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images

1 code implementation14 Jan 2022 Kai-Ni Wang, Xin Yang, Juzheng Miao, Lei LI, Jing Yao, Ping Zhou, Wufeng Xue, Guang-Quan Zhou, Xiahai Zhuang, Dong Ni

Extensive experimental results on a publicly available dataset from Myocardial pathology segmentation combining multi-sequence CMR (MyoPS 2020) demonstrate our method can achieve promising performance compared with other state-of-the-art methods.

Multi-Modality Cardiac Image Analysis with Deep Learning

no code implementations8 Nov 2021 Lei LI, Fuping Wu, Sihang Wang, Xiahai Zhuang

Accurate cardiac computing, analysis and modeling from multi-modality images are important for the diagnosis and treatment of cardiac disease.

Image Segmentation Semantic Segmentation +1

Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition

1 code implementation5 Sep 2021 Lei LI, Wangbin Ding, Liqun Huang, Xiahai Zhuang

In this work, we propose an automatic RV segmentation framework, where the information from long-axis (LA) views is utilized to assist the segmentation of short-axis (SA) views via information transition.

Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review

1 code implementation18 Jun 2021 Lei LI, Veronika A. Zimmer, Julia A. Schnabel, Xiahai Zhuang

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars.

AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs

no code implementations16 Jun 2021 Lei LI, Veronika A. Zimmer, Julia A. Schnabel, Xiahai Zhuang

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.

Domain Generalization Semantic Segmentation +1

A low-rank representation for unsupervised registration of medical images

no code implementations20 May 2021 Dengqiang Jia, Shangqi Gao, Qunlong Chen, Xinzhe Luo, Xiahai Zhuang

These methods estimate the parameterized transformations between pairs of moving and fixed images through the optimization of the network parameters during training.

Image Registration

Unsupervised Multi-Modality Registration Network based on Spatially Encoded Gradient Information

1 code implementation16 May 2021 Wangbin Ding, Lei LI, Xiahai Zhuang, Liqin Huang

However, it is still challenging to develop a multi-modality registration network due to the lack of robust criteria for network training.

VSpSR: Explorable Super-Resolution via Variational Sparse Representation

no code implementations17 Apr 2021 Hangqi Zhou, Chao Huang, Shangqi Gao, Xiahai Zhuang

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.


Learning-Based Algorithms for Vessel Tracking: A Review

no code implementations16 Dec 2020 Dengqiang Jia, Xiahai Zhuang

On the basis of the reviewed methods, the evaluation issues are introduced.

BIG-bench Machine Learning

Rank-One Network: An Effective Framework for Image Restoration

1 code implementation25 Nov 2020 Shangqi Gao, Xiahai Zhuang

The RO decomposition is developed to decompose a corrupted image into the RO components and residual.

Color Image Denoising Image Denoising +2

Anatomy Prior Based U-net for Pathology Segmentation with Attention

no code implementations17 Nov 2020 Yuncheng Zhou, Ke Zhang, Xinzhe Luo, Sihan Wang, Xiahai Zhuang

Pathological area segmentation in cardiac magnetic resonance (MR) images plays a vital role in the clinical diagnosis of cardiovascular diseases.


Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint

no code implementations29 Oct 2020 Chenyu Liu, Wangbin Ding, Lei LI, Zhen Zhang, Chenhao Pei, Liqin Huang, Xiahai Zhuang

Considering that multi-modal MR images can reflect different tumor biological properties, we develop a novel multi-modal tumor segmentation network (MMTSN) to robustly segment brain tumors based on multi-modal MR images.

Brain Tumor Segmentation Tumor Segmentation

Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information

no code implementations27 Aug 2020 Lei Li, Veronika A. Zimmer, Wangbin Ding, Fuping Wu, Liqin Huang, Julia A. Schnabel, Xiahai Zhuang

As the target domain could be unknown, we randomly generate a modality vector for the target modality in the style transfer stage, to simulate the domain shift for unknown domains.

Domain Generalization Image Segmentation +4

Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks

no code implementations15 Aug 2020 Wangbin Ding, Lei LI, Xiahai Zhuang, Liqin Huang

For label fusion, we adapt a few-shot learning network to measure the similarity of atlas and target patches.

Few-Shot Learning Image Registration

AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information

1 code implementation11 Aug 2020 Lei Li, Veronika A. Zimmer, Julia A. Schnabel, Xiahai Zhuang

In this work, we develop a new framework, namely AtrialJSQnet, where LA segmentation, scar projection onto the LA surface, and scar quantification are performed simultaneously in an end-to-end style.

MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation

1 code implementation28 Jun 2020 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 fixed images is optimized through backpropagation during training.

Heart Segmentation Image Registration +1

Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention

no code implementations23 Jun 2020 Lei Li, Xin Weng, Julia A. Schnabel, Xiahai Zhuang

Compared to conventional binary label based loss, the proposed SE loss can reduce noisy patches in the resulting segmentation, which is commonly seen for deep learning-based methods.

KLDivNet: An unsupervised neural network for multi-modality image registration

no code implementations23 Aug 2019 Yechong Huang, Tao Song, Jiahang Xu, Yinan Chen, Xiahai Zhuang

We then embed the KLDivNet into a registration network to achieve the unsupervised deformable registration for multi-modality images.

Image Registration Medical Image Registration

Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors

no code implementations18 Jun 2019 Qian Yue, Xinzhe Luo, Qing Ye, Lingchao Xu, Xiahai Zhuang

The proposed network, referred to as SRSCN, comprises a shape reconstruction neural network (SRNN) and a spatial constraint network (SCN).

Cardiac Segmentation Multi-Task Learning

Multi-scale deep neural networks for real image super-resolution

1 code implementation24 Apr 2019 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 tackle the difficulty, we develop two multi-scale deep neural networks (MsDNN) in this work.

Image Super-Resolution

A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images

no code implementations26 Feb 2019 Jiahang Xu, Fangyang Jiao, Yechong Huang, Xinzhe Luo, Qian Xu, Ling Li, Xueling Liu, Chuantao Zuo, Ping Wu, Xiahai Zhuang

Methods: In this paper, we proposed an automatic, end-to-end, multi-modality diagnosis framework, including segmentation, registration, feature generation and machine learning, to process the information of the striatum for the diagnosis of PD.

General Classification

Diagnosis of Alzheimer's Disease via Multi-modality 3D Convolutional Neural Network

no code implementations26 Feb 2019 Yechong Huang, Jiahang Xu, Yuncheng Zhou, Tong Tong, Xiahai Zhuang, the Alzheimer's Disease Neuroimaging Initiative

In this paper, we propose a novel convolutional neural network (CNN) to fuse the multi-modality information including T1-MRI and FDG-PDT images around the hippocampal area for the diagnosis of AD.

Image Classification

Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework

no code implementations21 Feb 2019 Lei Li, Fuping Wu, Guang Yang, Lingchao Xu, Tom Wong, Raad Mohiaddin, David Firmin, Jennifer Keegan, Xiahai Zhuang

Compared with the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0. 01).

PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

2 code implementations19 Dec 2018 Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Ben Glocker, Xiahai Zhuang, Pheng-Ann Heng

In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e. g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner.

Cardiac Segmentation Domain Adaptation +1

Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images

no code implementations22 Oct 2018 Fuping Wu, Lei LI, Guang Yang, Tom Wong, Raad Mohiaddin, David Firmin, Jennifer Keegan, Lingchao Xu, Xiahai Zhuang

We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE) image, and the other is an anatomical MRI from the same acquisition session.

Texture Classification

Atrial scars segmentation via potential learning in the graph-cuts framework

no code implementations22 Oct 2018 Lei Li, Fuping Wu, Guang Yang, Tom Wong, Raad Mohiaddin, David Firmin, Jenny Keegan, Lingchao Xu, Xiahai Zhuang

Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged as a routine scan for patients with atrial fibrillation (AF).

Multivariate mixture model for myocardium segmentation combining multi-source images

no code implementations28 Dec 2016 Xiahai Zhuang

The segmentation is a procedure of texture classification, and the MvMM is used to model the joint intensity distribution of the images.

Myocardium Segmentation Texture Classification

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