Search Results for author: Xinzhe Luo

Found 10 papers, 1 papers with code

$\mathcal{X}$-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.


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

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.


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

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

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

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