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 …
Artificial intelligence, machine learning, and cardiovascular disease
P Mathur, S Srivastava, X Xu… - Clinical Medicine …, 2020 - journals.sagepub.com
Artificial intelligence (AI)-based applications have found widespread applications in many
fields of science, technology, and medicine. The use of enhanced computing power of …
fields of science, technology, and medicine. The use of enhanced computing power of …
A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging
Segmentation of medical images, particularly late gadolinium-enhanced magnetic
resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first …
resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first …
Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers
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 …
[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 …
[HTML][HTML] Machine learning in cardiovascular magnetic resonance: basic concepts and applications
Abstract Machine learning (ML) is making a dramatic impact on cardiovascular magnetic
resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR …
resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR …
Convolutional neural network with shape prior applied to cardiac MRI segmentation
In this paper, we present a novel convolutional neural network architecture to segment
images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed …
images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed …
Disentangled representation learning in cardiac image analysis
Typically, a medical image offers spatial information on the anatomy (and pathology)
modulated by imaging specific characteristics. Many imaging modalities including Magnetic …
modulated by imaging specific characteristics. Many imaging modalities including Magnetic …
Concatenated and connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images
Segmentation of brain tumors from magnetic resonance imaging (MRI) data sets is of great
importance for improved diagnosis, growth rate prediction, and treatment planning …
importance for improved diagnosis, growth rate prediction, and treatment planning …
Left-ventricle quantification using residual U-Net
Estimating dimensional measurements of the left ventricle provides diagnostic values which
can be used to assess cardiac health and identify certain pathologies. In this paper we …
can be used to assess cardiac health and identify certain pathologies. In this paper we …