Automatic coronary calcium scoring using noncontrast and contrast CT images

Yang, Guanyu; Chen, Yang; Ning, Xiufang; Sun, Qi; Hu, Shu; Coatrieux, Jean-Louis

doi:10.1118/1.4945045

Cited by 40 publications

(32 citation statements)

References 32 publications

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“…step AI researchers and developers can take in order for their AI algorithms to be clinically implementable is to reach similar or better accuracy than human observers. From the above-mentioned task categories, segmentation tasks are currently the main types of tasks in cardiac CT that achieve almost similar accuracy to human readers with faster processing time than a human reader [22,36,37]. One of the reasons of the excellent performance of AI in segmentation tasks might be that the articles described in this review concern well-defined tasks, for example, finding the edge of LVM or detecting calcium spots that have a high contrast with surrounding tissue.…”

Section: Tablementioning

confidence: 99%

Application of artificial intelligence in cardiac CT: From basics to clinical practice

Oever

Vonder

Assen

et al. 2020

European Journal of Radiology

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Research into the possibilities of AI in cardiac CT has been growing rapidly in the last decade. With the rise of publicly available databases and AI algorithms, many researchers and clinicians have started investigations into the use of AI in the clinical workflow. This review is a comprehensive overview on the types of tasks and applications in which AI can aid the clinician in cardiac CT, and can be used as a primer for medical researchers starting in the field of AI. The applications of AI algorithms are explained and recent examples in cardiac CT of these algorithms are further elaborated on. The critical factors for implementation in the future are discussed.

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Section: Tablementioning

confidence: 99%

Application of artificial intelligence in cardiac CT: From basics to clinical practice

Oever

Vonder

Assen

et al. 2020

European Journal of Radiology

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“…With greater volumes of studies being performed and the potential of CAC scoring being added to other imaging protocols, there is a growing need for a more efficient and automated method [ 41 ]. The primary problem with the development of an automated system arises from the fact that calcifications are not solely present in the coronary arteries but also in surrounding cardiac tissues and cardiac valves, making automated detection complicated [ 42 ]. Several prior studies have attempted to solve this problem first by designating a region of interest around the heart on cardiac CT images and then identifying relevant CAC within the region by combinations of their relative position, texture, or size [ 43 - 46 ].…”

Section: Reviewmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

The Future of Concurrent Automated Coronary Artery Calcium Scoring on Screening Low-Dose Computed Tomography

Waltz

Kocher

Kahn

et al. 2020

Cureus

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Low-dose computed tomography (LDCT) has been extensively validated for lung cancer screening in selected patient populations. Additionally, the use of gated cardiac CT to assess coronary artery calcium (CAC) burden has been validated to determine a patient's risk for major cardiovascular adverse events. This is typically performed by calculating an Agatston score based on density and overall burden of calcified plaque within the coronary arteries. Patients that qualify for LDCT for lung cancer screening commonly share major risk factors for coronary artery disease and would frequently benefit from an additional gated cardiac CT for the assessment of CAC. Given the widespread use of LDCT for lung cancer screening, we evaluated current literature regarding the use of non-gated chest CT, specifically LDCT, for the detection and grading of coronary artery calcifications. Additionally, given the evolving and increasing use of artificial intelligence (AI) in the interpretation of radiologic studies, current literature for the use of AI in CAC assessment was reviewed.

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“…Recent years have witnessed the rapidly increasing number of new methods for vessel segmentation from different types of medical images, as evidenced by extensive reviews, such as a general review of this topic [1] and a review of 3D vessel segmentation [2]. As blood vessels can be seen as linear structures distributed at different orientations and scales in an image, various enhancement or filtering methods have been proposed to enhance the vascular structure: to remove undesired intensity variations in the image, and to suppress non-vascular structures and image noise, thereby easing the subsequent segmentation problem [3], [4]. The most well-known intensity-based filtering techniques include Hessian matrix-based filters [5]- [7], wavelet [8], matched filters [9]- [11], flux-based [12], [13], tensor-based filtering [14], [15], Gabor filters [16], and a more recent trainable filter named the combination of shifted filter responses (COSFIRE) [17], [18].…”

Section: Take Down Policymentioning

confidence: 99%

Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

Zhao

Zheng

Liu

et al. 2018

IEEE Trans. Med. Imaging

112

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Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis, and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors, such as poor contrast, inhomogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2-D/3-D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on eight publicly available datasets (six 2-D data sets, one 3-D data set, and one 3-D synthetic data set) demonstrate its superior performance to other state-of-the-art methods.

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Automatic coronary calcium scoring using noncontrast and contrast CT images

Abstract: The calcified lesions in the noncontrast CT images can be detected automatically by using the segmentation results of the aorta, heart, and coronary arteries obtained in the contrast CT images with a very high accuracy.

Cited by 40 publications

References 32 publications

Application of artificial intelligence in cardiac CT: From basics to clinical practice

Application of artificial intelligence in cardiac CT: From basics to clinical practice

The Future of Concurrent Automated Coronary Artery Calcium Scoring on Screening Low-Dose Computed Tomography

Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

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