Deep learning for cardiac image segmentation: a review
Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …
A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging
Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical
cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac …
cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac …
A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI
MR Avendi, A Kheradvar, H Jafarkhani - Medical image analysis, 2016 - Elsevier
Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI)
datasets is an essential step for calculation of clinical indices such as ventricular volume and …
datasets is an essential step for calculation of clinical indices such as ventricular volume and …
Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers
M Khened, VA Kollerathu, G Krishnamurthi - Medical image analysis, 2019 - Elsevier
Deep fully convolutional neural network (FCN) based architectures have shown great
potential in medical image segmentation. However, such architectures usually have millions …
potential in medical image segmentation. However, such architectures usually have millions …
A fully convolutional neural network for cardiac segmentation in short-axis MRI
PV Tran - arXiv preprint arXiv:1604.00494, 2016 - arxiv.org
Automated cardiac segmentation from magnetic resonance imaging datasets is an essential
step in the timely diagnosis and management of cardiac pathologies. We propose to tackle …
step in the timely diagnosis and management of cardiac pathologies. We propose to tackle …
Adversarial image synthesis for unpaired multi-modal cardiac data
A Chartsias, T Joyce, R Dharmakumar… - Simulation and Synthesis …, 2017 - Springer
This paper demonstrates the potential for synthesis of medical images in one modality (eg
MR) from images in another (eg CT) using a CycleGAN [24] architecture. The synthesis can …
MR) from images in another (eg CT) using a CycleGAN [24] architecture. The synthesis can …
Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis
In patients with coronary artery stenoses of intermediate severity, the functional significance
needs to be determined. Fractional flow reserve (FFR) measurement, performed during …
needs to be determined. Fractional flow reserve (FFR) measurement, performed during …
Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences
Automated left ventricular (LV) segmentation is crucial for efficient quantification of cardiac
function and morphology to aid subsequent management of cardiac pathologies. In this …
function and morphology to aid subsequent management of cardiac pathologies. In this …
Automatic segmentation of the right ventricle from cardiac MRI using a learning‐based approach
MR Avendi, A Kheradvar… - Magnetic resonance in …, 2017 - Wiley Online Library
Purpose This study aims to accurately segment the right ventricle (RV) from cardiac MRI
using a fully automatic learning‐based method. Methods The proposed method uses deep …
using a fully automatic learning‐based method. Methods The proposed method uses deep …
Automated localization and segmentation techniques for B-mode ultrasound images: A review
B-mode ultrasound imaging is used extensively in medicine. Hence, there is a need to have
efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions …
efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions …