[HTML][HTML] 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 …
State-of-the-art deep learning in cardiovascular image analysis
Cardiovascular imaging is going to change substantially in the next decade, fueled by the
deep learning revolution. For medical professionals, it is important to keep track of these …
deep learning revolution. For medical professionals, it is important to keep track of these …
Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge
The emergence of deep learning has considerably advanced the state-of-the-art in cardiac
magnetic resonance (CMR) segmentation. Many techniques have been proposed over the …
magnetic resonance (CMR) segmentation. Many techniques have been proposed over the …
Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac
magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish …
magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish …
[HTML][HTML] Reference ranges (“normal values”) for cardiovascular magnetic resonance (CMR) in adults and children: 2020 update
N Kawel-Boehm, SJ Hetzel… - Journal of …, 2020 - Elsevier
Cardiovascular magnetic resonance (CMR) enables assessment and quantification of
morphological and functional parameters of the heart, including chamber size and function …
morphological and functional parameters of the heart, including chamber size and function …
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 …
[HTML][HTML] Test-time adaptable neural networks for robust medical image segmentation
Abstract Convolutional Neural Networks (CNNs) work very well for supervised learning
problems when the training dataset is representative of the variations expected to be …
problems when the training dataset is representative of the variations expected to be …
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 …
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image
analysis methods. Given their scientific impact, it is surprising that a critical analysis of …
analysis methods. Given their scientific impact, it is surprising that a critical analysis of …
[HTML][HTML] Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge
Abstract Knowledge of whole heart anatomy is a prerequisite for many clinical applications.
Whole heart segmentation (WHS), which delineates substructures of the heart, can be very …
Whole heart segmentation (WHS), which delineates substructures of the heart, can be very …