TTN: Topological Transformer Network for Automated Coronary Artery Branch Labeling in Cardiac CT Angiography

Autor: Yuyang Zhang, Gongning Luo, Wei Wang, Shaodong Cao, Suyu Dong, Daren Yu, Xiaoyun Wang, Kuanquan Wang

Publikováno v: IEEE Journal of Translational Engineering in Health and Medicine, Vol 12, Pp 129-139 (2024)

Objective: Existing methods for automated coronary artery branch labeling in cardiac CT angiography face two limitations: 1) inability to model overall correlation of branches, since differences between branches cannot be captured directly. 2) a seri

Externí odkaz: https://doaj.org/article/c74f762287e048cc93a2d66b7ec00753

MAE-TransRNet: An improved transformer-ConvNet architecture with masked autoencoder for cardiac MRI registration

Autor: Xin Xiao, Suyu Dong, Yang Yu, Yan Li, Guangyuan Yang, Zhaowen Qiu

Publikováno v: Frontiers in Medicine, Vol 10 (2023)

The heart is a relatively complex non-rigid motion organ in the human body. Quantitative motion analysis of the heart takes on a critical significance to help doctors with accurate diagnosis and treatment. Moreover, cardiovascular magnetic resonance

Externí odkaz: https://doaj.org/article/3eda1d11dfa94dc7a339cdf117df64cc

Computational Cardiac Modeling Reveals Mechanisms of Ventricular Arrhythmogenesis in Long QT Syndrome Type 8: CACNA1C R858H Mutation Linked to Ventricular Fibrillation

Autor: Jieyun Bai, Kuanquan Wang, Yashu Liu, Yacong Li, Cuiping Liang, Gongning Luo, Suyu Dong, Yongfeng Yuan, Henggui Zhang

Publikováno v: Frontiers in Physiology, Vol 8 (2017)

Functional analysis of the L-type calcium channel has shown that the CACNA1C R858H mutation associated with severe QT interval prolongation may lead to ventricular fibrillation (VF). This study investigated multiple potential mechanisms by which the

Externí odkaz: https://doaj.org/article/2cbd74b30a8a44648cafde7af27aa6bb

A Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography

Autor: Qince Li, Shaodong Cao, Suyu Dong, Kuanquan Wang, Gongning Luo, Henggui Zhang

Publikováno v: BioMed Research International, Vol 2018 (2018)
Dong, S, Luo, G, Wang, K, Cao, S, Li, Q & Zhang, H 2018, ' A Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography ', BioMed Research International, vol. 2018, 5682365, pp. 1-16 . https://doi.org/10.1155/2018/5682365
BioMed Research International

Segmentation of the left ventricle (LV) from three-dimensional echocardiography (3DE) plays a key role in the clinical diagnosis of the LV function. In this work, we proposed a new automatic method for the segmentation of LV, based on the fully convo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93f1a3cbb6a3d458b3d29b88f6809f7d
https://doi.org/10.1155/2018/5682365

Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography

Autor: Bo Chen, Clara M. Tam, Kuanquan Wang, Gongning Luo, Wei Wang, Shuo Li, Shaodong Cao, Henggui Zhang, Suyu Dong

Publikováno v: Medical image analysis. 61

We proposed a novel efficient method for 3D left ventricle (LV) segmentation on echocardiography, which is important for cardiac disease diagnosis. The proposed method effectively overcame the 3D echocardiography's challenges: high dimensional data,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb952382e2973870c40c43ccec07d189
https://pubmed.ncbi.nlm.nih.gov/32007701