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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
Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation
In cardiac magnetic resonance imaging, fully-automatic segmentation of the
heart enables precise structural and functional measurements to be taken, e.g.
from short-axis MR images of the left-ventricle. In this work we propose a
recurrent fully-convolutional network (RFCN) that learns image representations
from the full stack of 2D slices and has the ability to leverage inter-slice
spatial dependences through internal memory units. RFCN combines anatomical
detection and segmentation into a single architecture that is trained
end-to-end thus significantly reducing computational time, simplifying the
segmentation pipeline, and potentially enabling real-time applications. We
report on an investigation of RFCN using two datasets, including the publicly
available MICCAI 2009 Challenge dataset. Comparisons have been carried out
between fully convolutional networks and deep restricted Boltzmann machines,
including a recurrent version that leverages inter-slice spatial correlation.
Our studies suggest that RFCN produces state-of-the-art results and can
substantially improve the delineation of contours near the apex of the heart.Comment: MICCAI Workshop RAMBO 201
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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