3,894 research outputs found
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
Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries
Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the
most frequent risk factor for thrombosis and acute coronary syndrome.
Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess
cap thickness, which provides an opportunity to assess plaque vulnerability. We
developed an automated method that can detect lipidous plaque and assess
fibrous cap thickness in IVOCT images. This study analyzed a total of 4,360
IVOCT image frames of 77 lesions among 41 patients. To improve segmentation
performance, preprocessing included lumen segmentation, pixel-shifting, and
noise filtering on the raw polar (r, theta) IVOCT images. We used the
DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After
lipid detection, we automatically detected the outer border of the fibrous cap
using a special dynamic programming algorithm and assessed the cap thickness.
Our method provided excellent discriminability of lipid plaque with a
sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid
angle measurements between two analysts following editing of our automated
software, we found good agreement by Bland-Altman analysis (difference 6.7+/-17
degree; mean 196 degree). Our method accurately detected the fibrous cap from
the detected lipid plaque. Automated analysis required a significant
modification for only 5.5% frames. Furthermore, our method showed a good
agreement of fibrous cap thickness between two analysts with Bland-Altman
analysis (4.2+/-14.6 micron; mean 175 micron), indicating little bias between
users and good reproducibility of the measurement. We developed a fully
automated method for fibrous cap quantification in IVOCT images, resulting in
good agreement with determinations by analysts. The method has great potential
to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.Comment: 18 pages, 9 figure
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Quantification of atherosclerotic plaque volume in coronary arteries by computed tomographic angiography in subjects with and without diabetes.
BackgroundDiabetes mellitus (DM) is considered a cardiovascular risk factor. The aim of this study was to analyze the prevalence and volume of coronary artery plaque in patients with diabetes mellitus (DM) vs. those without DM.MethodsThis study recruited consecutive patients who underwent coronary computed tomography (CT) angiography (CCTA) between October 2016 and November 2017. Personal information including conventional cardiovascular risk factors was collected. Plaque phenotypes were automatically calculated for volume of different component. The volume of different plaque was compared between DM patients and those without DM.ResultsAmong 6381 patients, 931 (14.59%) were diagnosed with DM. The prevalence of plaque in DM subjects was higher compared with nondiabetic group significantly (48.34% vs. 33.01%, χ = 81.84, P < 0.001). DM was a significant risk factor for the prevalence of plaque in a multivariate model (odds ratio [OR] = 1.465, 95% CI: 1.258-1.706, P < 0.001). The volume of total plaque and any plaque subtypes in the DM subjects was greater than those in nondiabetic patients significantly (P < 0.001).ConclusionThe coronary artery atherosclerotic plaques were significantly higher in diabetic patients than those in non-diabetic patients
Automated classification of coronary plaque calcification in OCT pullbacks with 3D deep neural networks
Significance: Detection and characterization of coronary atherosclerotic plaques often need reviews of a large number of optical coherence tomography (OCT) imaging slices to make a clinical decision. However, it is a challenge to manually review all the slices and consider the interrelationship between adjacent slices.
Approach: Inspired by the recent success of deep convolutional network on the classification of medical images, we proposed a ResNet-3D network for classification of coronary plaque calcification in OCT pullbacks. The ResNet-3D network was initialized with a trained ResNet-50 network and a three-dimensional convolution filter filled with zeros padding and non-zeros padding with a convolutional filter. To retrain ResNet-50, we used a dataset of ∼4860 OCT images, derived by 18 entire pullbacks from different patients. In addition, we investigated a two-phase training method to address the data imbalance. For an improved performance, we evaluated different input sizes for the ResNet-3D network, such as 3, 5, and 7 OCT slices. Furthermore, we integrated all ResNet-3D results by majority voting.
Results: A comparative analysis proved the effectiveness of the proposed ResNet-3D networks against ResNet-2D network in the OCT dataset. The classification performance (F1-scores = 94 % for non-zeros padding and F1-score = 96 % for zeros padding) demonstrated the potential of convolutional neural networks (CNNs) in classifying plaque calcification.
Conclusions: This work may provide a foundation for further work in extending the CNN to voxel segmentation, which may lead to a supportive diagnostic tool for assessment of coronary plaque vulnerability
Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images
Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture.
Intravascular optical coherence tomography (IVOCT) enables identification of
fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque
vulnerability. We developed a fully-automated deep learning method for FC
segmentation. This study included 32,531 images across 227 pullbacks from two
registries. Images were semi-automatically labeled using our OCTOPUS with
expert editing using established guidelines. We employed preprocessing
including guidewire shadow detection, lumen segmentation, pixel-shifting, and
Gaussian filtering on raw IVOCT (r,theta) images. Data were augmented in a
natural way by changing theta in spiral acquisitions and by changing intensity
and noise values. We used a modified SegResNet and comparison networks to
segment FCs. We employed transfer learning from our existing much larger,
fully-labeled calcification IVOCT dataset to reduce deep-learning training.
Overall, our method consistently delivered better FC segmentation results
(Dice: 0.837+/-0.012) than other deep-learning methods. Transfer learning
reduced training time by 84% and reduced the need for more training samples.
Our method showed a high level of generalizability, evidenced by
highly-consistent segmentations across five-fold cross-validation (sensitivity:
85.0+/-0.3%, Dice: 0.846+/-0.011) and the held-out test (sensitivity: 84.9%,
Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness
with ground truth (2.95+/-20.73 um), giving clinically insignificant bias.
There was excellent reproducibility in pre- and post-stenting pullbacks
(average FC angle: 200.9+/-128.0 deg / 202.0+/-121.1 deg). Our method will be
useful for multiple research purposes and potentially for planning stent
deployments that avoid placing a stent edge over an FC.Comment: 24 pages, 9 figures, 2 tables, 2 supplementary figures, 3
supplementary table
Clinical validation of an algorithm for rapid and accurate automated segmentation of intracoronary optical coherence tomography images
Objectives: The analysis of intracoronary optical coherence tomography (OCT) images is based on manual identification of the lumen contours and relevant structures. However, manual image segmentation is a cumbersome and time-consuming process, subject to significant intra- and inter-observer variability. This
study aims to present and validate a fully-automated method for segmentation of intracoronary OCT images.
Methods: We studied 20 coronary arteries (mean length = 39.7 ± 10.0 mm) from 20 patients who underwent a
clinically-indicated cardiac catheterization. The OCT images (n = 1812) were segmented manually, as well as
with a fully-automated approach. A semi-automated variation of the fully-automated algorithm was also applied. Using certain lumen size and lumen shape characteristics, the fully- and semi-automated segmentation algorithms were validated over manual segmentation, which was considered as the gold standard.
Results: Linear regression and Bland–Altman analysis demonstrated that both the fully-automated and semiautomated segmentation had a very high agreement with the manual segmentation, with the semi-automated
approach being slightly more accurate than the fully-automated method. The fully-automated and semiautomated OCT segmentation reduced the analysis time by more than 97% and 86%, respectively, compared to manual segmentation.
Conclusions: In the current work we validated a fully-automated OCT segmentation algorithm, as well as a semiautomated variation of it in an extensive “real-life” dataset of OCT images. The study showed that our algorithm can perform rapid and reliable segmentation of OCT images
Optical coherence tomography for the assessment of coronary atherosclerosis and vessel response after stent implantation
Optical Coherence Tomography (OCT) is a light-based imaging modality that can provide in vivo high-resolution images of the coronary artery with a level of resolution (axial 10-20 µm) ten times higher than intravascular ultrasound. The technique, uses low-coherent near infrarred light to create high-resolution cross sectional images of the vessel. The technology refinement achieved in the last years has made this imaging modality less procedurally demanding opening its possibilities for clinical use. The present thesis provides im
AUTOMATED QUANTITATIVE ASSESSMENT OF CORONARY CALCIFICATION USING INTRAVASCULAR ULTRASOUND
Coronary calcification represents a challenge in the treatment of coronary artery disease by stent placement. It negatively affects stent expansion and has been related to future adverse cardiac events. Intravascular
ultrasound (IVUS) is known for its high sensitivity in detecting coronary calcification. At present, automated quantification of calcium as detected by IVUS is not available. For this reason, we developed and validated an optimized
framework for accurate automated detection and quantification of calcified plaque in coronary atherosclerosis as
seen by IVUS. Calcified lesions were detected by training a supported vector classifier per IVUS A-line on manually
annotated IVUS images, followed by post-processing using regional information. We applied our framework to 35
IVUS pullbacks from each of the three commonly used IVUS systems. Cross-validation accuracy for each system
was >0.9, and the testing accuracy was 0.87, 0.89 and 0.89 for the three systems. Using the detection result, we propose an IVUS calcium score, based on the fraction of calcium-positive A-lines in a pullback segment, to quantify
the extent of calcified plaque. The high accuracy of the proposed classifier suggests that it may provide a robust
and accurate tool to assess the presence and amount of coronary calcification and, thus, may play a role in imageguided coronary interventions. (E-mail: [email protected]
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