44 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
Automatic Segmentation of the Left Ventricle in Cardiac CT Angiography Using Convolutional Neural Network
Accurate delineation of the left ventricle (LV) is an important step in
evaluation of cardiac function. In this paper, we present an automatic method
for segmentation of the LV in cardiac CT angiography (CCTA) scans. Segmentation
is performed in two stages. First, a bounding box around the LV is detected
using a combination of three convolutional neural networks (CNNs).
Subsequently, to obtain the segmentation of the LV, voxel classification is
performed within the defined bounding box using a CNN. The study included CCTA
scans of sixty patients, fifty scans were used to train the CNNs for the LV
localization, five scans were used to train LV segmentation and the remaining
five scans were used for testing the method. Automatic segmentation resulted in
the average Dice coefficient of 0.85 and mean absolute surface distance of 1.1
mm. The results demonstrate that automatic segmentation of the LV in CCTA scans
using voxel classification with convolutional neural networks is feasible.Comment: This work has been published as: Zreik, M., Leiner, T., de Vos, B.
D., van Hamersvelt, R. W., Viergever, M. A., I\v{s}gum, I. (2016, April).
Automatic segmentation of the left ventricle in cardiac CT angiography using
convolutional neural networks. In Biomedical Imaging (ISBI), 2016 IEEE 13th
International Symposium on (pp. 40-43). IEE
Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis
In patients with coronary artery stenoses of intermediate severity, the
functional significance needs to be determined. Fractional flow reserve (FFR)
measurement, performed during invasive coronary angiography (ICA), is most
often used in clinical practice. To reduce the number of ICA procedures, we
present a method for automatic identification of patients with functionally
significant coronary artery stenoses, employing deep learning analysis of the
left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The
study includes consecutively acquired CCTA scans of 166 patients with FFR
measurements. To identify patients with a functionally significant coronary
artery stenosis, analysis is performed in several stages. First, the LV
myocardium is segmented using a multiscale convolutional neural network (CNN).
To characterize the segmented LV myocardium, it is subsequently encoded using
unsupervised convolutional autoencoder (CAE). Thereafter, patients are
classified according to the presence of functionally significant stenosis using
an SVM classifier based on the extracted and clustered encodings. Quantitative
evaluation of LV myocardium segmentation in 20 images resulted in an average
Dice coefficient of 0.91 and an average mean absolute distance between the
segmented and reference LV boundaries of 0.7 mm. Classification of patients was
evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation
experiments and resulted in an area under the receiver operating characteristic
curve of 0.74 +- 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the
corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results
demonstrate that automatic analysis of the LV myocardium in a single CCTA scan
acquired at rest, without assessment of the anatomy of the coronary arteries,
can be used to identify patients with functionally significant coronary artery
stenosis.Comment: This paper was submitted in April 2017 and accepted in November 2017
for publication in Medical Image Analysis. Please cite as: Zreik et al.,
Medical Image Analysis, 2018, vol. 44, pp. 72-8
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
Deep Learning from Dual-Energy Information for Whole-Heart Segmentation in Dual-Energy and Single-Energy Non-Contrast-Enhanced Cardiac CT
Deep learning-based whole-heart segmentation in coronary CT angiography
(CCTA) allows the extraction of quantitative imaging measures for
cardiovascular risk prediction. Automatic extraction of these measures in
patients undergoing only non-contrast-enhanced CT (NCCT) scanning would be
valuable. In this work, we leverage information provided by a dual-layer
detector CT scanner to obtain a reference standard in virtual non-contrast
(VNC) CT images mimicking NCCT images, and train a 3D convolutional neural
network (CNN) for the segmentation of VNC as well as NCCT images.
Contrast-enhanced acquisitions on a dual-layer detector CT scanner were
reconstructed into a CCTA and a perfectly aligned VNC image. In each CCTA
image, manual reference segmentations of the left ventricular (LV) myocardium,
LV cavity, right ventricle, left atrium, right atrium, ascending aorta, and
pulmonary artery trunk were obtained and propagated to the corresponding VNC
image. These VNC images and reference segmentations were used to train 3D CNNs
for automatic segmentation in either VNC images or NCCT images. Automatic
segmentations in VNC images showed good agreement with reference segmentations,
with an average Dice similarity coefficient of 0.897 \pm 0.034 and an average
symmetric surface distance of 1.42 \pm 0.45 mm. Volume differences [95%
confidence interval] between automatic NCCT and reference CCTA segmentations
were -19 [-67; 30] mL for LV myocardium, -25 [-78; 29] mL for LV cavity, -29
[-73; 14] mL for right ventricle, -20 [-62; 21] mL for left atrium, and -19
[-73; 34] mL for right atrium, respectively. In 214 (74%) NCCT images from an
independent multi-vendor multi-center set, two observers agreed that the
automatic segmentation was mostly accurate or better. This method might enable
quantification of additional cardiac measures from NCCT images for improved
cardiovascular risk prediction
Imaging of pediatric great vessel stents: Computed tomography or magnetic resonance imaging?
__Background:__ Complications might occur after great vessel stent implantation in children. Therefore follow- up using imaging is warranted.
__Purpose:__ To determine the optimal imaging modality for the assessment of stents used to treat great vessel obstructions in children.
__Material and methods:__ Five different large vessel stents were evaluated in an in-vitro setting. All stents were expanded to the maximal vendor recommended diameter (20mm; n = 4 or 10mm; n = 1), placed in an anthropomorphic chest phantom and imaged with a 256-slice CT-scanner. MRI images were acquired at 1.5T using a multi-slice T2-weighted turbo spin echo, an RFspoiled three-dimensional T1-weighted Fast Field Echo and a balanced turbo field echo 3D seq