Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier
Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a
prerequisite for evaluation of stenoses and atherosclerotic plaque. In this work, we propose …
prerequisite for evaluation of stenoses and atherosclerotic plaque. In this work, we propose …
Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms
Efficiently obtaining a reliable coronary artery centerline from computed tomography
angiography data is relevant in clinical practice. Whereas numerous methods have been …
angiography data is relevant in clinical practice. Whereas numerous methods have been …
Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes
Y Zheng, M Loziczonek, B Georgescu… - Medical Imaging …, 2011 - spiedigitallibrary.org
Automatic coronary centerline extraction and lumen segmentation facilitate the diagnosis of
coronary artery disease (CAD), which is a leading cause of death in developed countries …
coronary artery disease (CAD), which is a leading cause of death in developed countries …
Joint coronary centerline extraction and lumen segmentation from ccta using cnntracker and vascular graph convolutional network
R Gao, Z Hou, J Li, H Han, B Lu… - 2021 IEEE 18th …, 2021 - ieeexplore.ieee.org
Automatic analysis of coronary artery in coronary computed tomography angiography
(CCTA) is important for clinicians to diagnose and evaluate coronary artery disease (CAD) …
(CCTA) is important for clinicians to diagnose and evaluate coronary artery disease (CAD) …
Discriminative coronary artery tracking via 3D CNN in cardiac CT angiography
Extraction of the coronary artery centerline from cardiac CT angiography (CCTA) is a
challenging yet prerequisite task for subsequent diagnosis in clinical practice. In this paper …
challenging yet prerequisite task for subsequent diagnosis in clinical practice. In this paper …
Multi-resolution 3d convolutional neural networks for automatic coronary centerline extraction in cardiac CT angiography scans
Z Salahuddin, M Lenga… - 2021 IEEE 18th …, 2021 - ieeexplore.ieee.org
We propose a deep learning-based automatic coronary artery tree centerline tracker
(AuCoTrack) extending the vessel work of [1]. A multi-resolution 3D Convolutional Neural …
(AuCoTrack) extending the vessel work of [1]. A multi-resolution 3D Convolutional Neural …
LIVE-Net: Comprehensive 3D vessel extraction framework in CT angiography
Q Sun, J Yang, S Zhao, C Chen, Y Hou, Y Yuan… - Computers in Biology …, 2023 - Elsevier
The extraction of vessels from computed tomography angiography (CTA) is significant in
diagnosing and evaluating vascular diseases. However, due to the anatomical complexity …
diagnosing and evaluating vascular diseases. However, due to the anatomical complexity …
[PDF][PDF] 3D segmentation in the clinic: A grand challenge II-coronary artery tracking
In this paper the Coronary Artery Tracking competition, which was part of the workshop:" 3D
Segmentation in the Clinic: A Grand Challenge II" is described. This workshop was held …
Segmentation in the Clinic: A Grand Challenge II" is described. This workshop was held …
Branch-aware double DQN for centerline extraction in coronary CT angiography
Accurate coronary artery centerline is essential for coronary stenosis analysis and
atherosclerotic plaque analysis. However, the existence of many branches makes accurate …
atherosclerotic plaque analysis. However, the existence of many branches makes accurate …
CenterlineNet: Automatic coronary artery centerline extraction for computed tomographic angiographic images using convolutional neural network architectures
S Rjiba, T Urruty, P Bourdon… - … on Image Processing …, 2020 - ieeexplore.ieee.org
The prevention of cardiovascular diseases starts by a thorough examination of the coronary
artery vessels for atherosclerotic plaques existence. By combining deep learning …
artery vessels for atherosclerotic plaques existence. By combining deep learning …