59 research outputs found
Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier
Coronary artery centerline extraction in cardiac CT angiography (CCTA) images
is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We
propose an algorithm that extracts coronary artery centerlines in CCTA using a
convolutional neural network (CNN).
A 3D dilated CNN is trained to predict the most likely direction and radius
of an artery at any given point in a CCTA image based on a local image patch.
Starting from a single seed point placed manually or automatically anywhere in
a coronary artery, a tracker follows the vessel centerline in two directions
using the predictions of the CNN. Tracking is terminated when no direction can
be identified with high certainty.
The CNN was trained using 32 manually annotated centerlines in a training set
consisting of 8 CCTA images provided in the MICCAI 2008 Coronary Artery
Tracking Challenge (CAT08). Evaluation using 24 test images of the CAT08
challenge showed that extracted centerlines had an average overlap of 93.7%
with 96 manually annotated reference centerlines. Extracted centerline points
were highly accurate, with an average distance of 0.21 mm to reference
centerline points. In a second test set consisting of 50 CCTA scans, 5,448
markers in the coronary arteries were used as seed points to extract single
centerlines. This showed strong correspondence between extracted centerlines
and manually placed markers. In a third test set containing 36 CCTA scans,
fully automatic seeding and centerline extraction led to extraction of on
average 92% of clinically relevant coronary artery segments.
The proposed method is able to accurately and efficiently determine the
direction and radius of coronary arteries. The method can be trained with
limited training data, and once trained allows fast automatic or interactive
extraction of coronary artery trees from CCTA images.Comment: Accepted in Medical Image Analysi
Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography
In patients with obstructive coronary artery disease, the functional
significance of a coronary artery stenosis needs to be determined to guide
treatment. This is typically established through fractional flow reserve (FFR)
measurement, performed during invasive coronary angiography (ICA). We present a
method for automatic and non-invasive detection of patients requiring ICA,
employing deep unsupervised analysis of complete coronary arteries in cardiac
CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187
patients, 137 of them underwent invasive FFR measurement in 192 different
coronary arteries. These FFR measurements served as a reference standard for
the functional significance of the coronary stenosis. The centerlines of the
coronary arteries were extracted and used to reconstruct straightened
multi-planar reformatted (MPR) volumes. To automatically identify arteries with
functionally significant stenosis that require ICA, each MPR volume was encoded
into a fixed number of encodings using two disjoint 3D and 1D convolutional
autoencoders performing spatial and sequential encodings, respectively.
Thereafter, these encodings were employed to classify arteries using a support
vector machine classifier. The detection of coronary arteries requiring
invasive evaluation, evaluated using repeated cross-validation experiments,
resulted in an area under the receiver operating characteristic curve of on the artery-level, and on the patient-level. The
results demonstrate the feasibility of automatic non-invasive detection of
patients that require ICA and possibly subsequent coronary artery intervention.
This could potentially reduce the number of patients that unnecessarily undergo
ICA.Comment: This work has been accepted to IEEE TMI for publicatio
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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
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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