8,280 research outputs found
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
Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
Heavy smokers undergoing screening with low-dose chest CT are affected by
cardiovascular disease as much as by lung cancer. Low-dose chest CT scans
acquired in screening enable quantification of atherosclerotic calcifications
and thus enable identification of subjects at increased cardiovascular risk.
This paper presents a method for automatic detection of coronary artery,
thoracic aorta and cardiac valve calcifications in low-dose chest CT using two
consecutive convolutional neural networks. The first network identifies and
labels potential calcifications according to their anatomical location and the
second network identifies true calcifications among the detected candidates.
This method was trained and evaluated on a set of 1744 CT scans from the
National Lung Screening Trial. To determine whether any reconstruction or only
images reconstructed with soft tissue filters can be used for calcification
detection, we evaluated the method on soft and medium/sharp filter
reconstructions separately. On soft filter reconstructions, the method achieved
F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta,
aortic valve and mitral valve calcifications, respectively. On sharp filter
reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively.
Linearly weighted kappa coefficients for risk category assignment based on per
subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter
reconstructions, respectively. These results demonstrate that the presented
method enables reliable automatic cardiovascular risk assessment in all
low-dose chest CT scans acquired for lung cancer screening
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
Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease
We propose an automatic method using dilated convolutional neural networks
(CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR
(CMR) of patients with congenital heart disease (CHD).
Ten training and ten test CMR scans cropped to an ROI around the heart were
provided in the MICCAI 2016 HVSMR challenge. A dilated CNN with a receptive
field of 131x131 voxels was trained for myocardium and blood pool segmentation
in axial, sagittal and coronal image slices. Performance was evaluated within
the HVSMR challenge.
Automatic segmentation of the test scans resulted in Dice indices of
0.800.06 and 0.930.02, average distances to boundaries of
0.960.31 and 0.890.24 mm, and Hausdorff distances of 6.133.76
and 7.073.01 mm for the myocardium and blood pool, respectively.
Segmentation took 41.514.7 s per scan.
In conclusion, dilated CNNs trained on a small set of CMR images of CHD
patients showing large anatomical variability provide accurate myocardium and
blood pool segmentations
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