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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 papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
Computationally efficient cardiac views projection using 3D Convolutional Neural Networks
4D Flow is an MRI sequence which allows acquisition of 3D images of the
heart. The data is typically acquired volumetrically, so it must be reformatted
to generate cardiac long axis and short axis views for diagnostic
interpretation. These views may be generated by placing 6 landmarks: the left
and right ventricle apex, and the aortic, mitral, pulmonary, and tricuspid
valves. In this paper, we propose an automatic method to localize landmarks in
order to compute the cardiac views. Our approach consists of first calculating
a bounding box that tightly crops the heart, followed by a landmark
localization step within this bounded region. Both steps are based on a 3D
extension of the recently introduced ENet. We demonstrate that the long and
short axis projections computed with our automated method are of equivalent
quality to projections created with landmarks placed by an experienced cardiac
radiologist, based on a blinded test administered to a different cardiac
radiologist
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