605 research outputs found
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
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
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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
An automatic deep learning approach for coronary artery calcium segmentation
Coronary artery calcium (CAC) is a significant marker of atherosclerosis and
cardiovascular events. In this work we present a system for the automatic
quantification of calcium score in ECG-triggered non-contrast enhanced cardiac
computed tomography (CT) images. The proposed system uses a supervised deep
learning algorithm, i.e. convolutional neural network (CNN) for the
segmentation and classification of candidate lesions as coronary or not,
previously extracted in the region of the heart using a cardiac atlas. We
trained our network with 45 CT volumes; 18 volumes were used to validate the
model and 56 to test it. Individual lesions were detected with a sensitivity of
91.24%, a specificity of 95.37% and a positive predicted value (PPV) of 90.5%;
comparing calcium score obtained by the system and calcium score manually
evaluated by an expert operator, a Pearson coefficient of 0.983 was obtained. A
high agreement (Cohen's k = 0.879) between manual and automatic risk prediction
was also observed. These results demonstrated that convolutional neural
networks can be effectively applied for the automatic segmentation and
classification of coronary calcifications
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
Machine learning applications in cardiac computed tomography: a composite systematic review
Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT
Application of AI in cardiovascular multimodality imaging
Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging
Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography
Purpose: The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data. Materials and Methods: Under an IRB waiver and in HIPAA compliance, a total of 194 patients who had undergone CCTA were retrospectively included. Two observers independently evaluated the image quality and recorded the presence of CAC in the right (RCA), the combination of left main and left anterior descending (LM-LAD), and left circumflex (LCx) coronary arteries. Noncontrast CACS scans were allowed to be used in cases of uncertainty. Heart and coronary artery centerline detection and labeling were automatically performed. Presence of CAC was assessed by a RNN-LSTM. The algorithm's overall and per-vessel sensitivity, specificity, and diagnostic accuracy were calculated. Results: CAC was absent in 84 and present in 110 patients. As regards CCTA, the median subjective image quality, signal-to-noise ratio, and contrast-to-noise ratio were 3.0, 13.0, and 11.4. A total of 565 vessels were evaluated. On a per-vessel basis, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 93.1% (confidence interval [CI], 84.3%-96.7%), 82.76% (CI, 74.6%-89.4%), and 86.7% (CI, 76.8%-87.9%), respectively, for the RCA, 93.1% (CI, 86.4%-97.7%), 95.5% (CI, 88.77%-98.75%), and 94.2% (CI. 90.2%-94.6%), respectively, for the LM-LAD, and 89.9% (CI, 80.2%-95.8%), 90.0% (CI, 83.2%-94.7%), and 89.9% (CI, 85.0%-94.1%), respectively, for the LCx. The overall sensitivity, specificity, and diagnostic accuracy were 92.1% (CI, 92.1%-95.2%), 88.9% (CI. 84.9%-92.1%), and 90.3% (CI, 88.0%-90.0%), respectively. When accounting for image quality, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 76.2%, 87.5%, and 82.2%, respectively, for poor-quality data sets and 93.3%, 89.2% and 90.9%, respectively, when data sets rated adequate or higher were combined. Conclusion: The proposed RNN-LSTM demonstrated high diagnostic accuracy for the detection of CAC from CCTA
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