Automated aortic calcification detection in low-dose chest CT images

Xie, Yiting; Htwe, Yu Maw; Padgett, Jennifer; Henschke, Claudia I.; Yankelevitz, David F.; Reeves, Anthony P.

doi:10.1117/12.2043810

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…In comparison, we reported here a sensitivity of 90 % at an average false positive volume of 213 mm 3 and a positive predictive value of 89 % in 310 scans reconstructed with soft filter kernels. Xie et al [31] reported only the correlation with manual scores as R 2 = 0.98 after performing linear regression for TAC volume in 45 scans.…”

Section: G Comparison With Other Methodsmentioning

confidence: 99%

“…For calcium scoring in the thoracic aorta in chest CT, few automatic methods have been published. All methods first perform a segmentation of the aorta followed by either rule-based calcification detection using auxiliary segmentations of trachea and spine [30], [31] or calcification detection based on machine learning [32]. The machine learning approach uses kNN classifiers with features similar to those used by methods for coronary calcium detection: various spatial features derived from the segmentation of the aorta, volume of the potential calcification and texture features.…”

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confidence: 99%

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Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions

Leßmann

Ginneken

Zreik

et al. 2018

IEEE Trans. Med. Imaging

217

215

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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 F 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 F 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.

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Section: G Comparison With Other Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions

Leßmann

Ginneken

Zreik

et al. 2018

IEEE Trans. Med. Imaging

217

215

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show abstract

“…[4][5][6][7][8][9] Lung cancer screening programs include a large number of (former) heavy smokers who are at increased risk of lung cancer, but also of other smoking related diseases, including CVD and osteoporosis. It has already been shown that chest CT scans as acquired in lung cancer screening enable identification of subjects at risk of a cardiovascular event based on the amount of coronary and aortic calcifications.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association study of coronary and aortic calcification in lung cancer screening CT

et al. 2016

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Arterial calcification has been related to cardiovascular disease (CVD) and osteoporosis. However, little is known about the role of genetics and exact pathways leading to arterial calcification and its relation to bone density changes indicating osteoporosis. In this study, we conducted a genome-wide association study of arterial calcification burden, followed by a look-up of known single nucleotide polymorphisms (SNPs) for coronary artery disease (CAD) and myocardial infarction (MI), and bone mineral density (BMD) to test for a shared genetic basis between the traits.The study included a subcohort of the Dutch-Belgian lung cancer screening trial comprised of 2,561 participants. Participants underwent baseline CT screening in one of two hospitals participating in the trial. Low-dose chest CT images were acquired without contrast enhancement and without ECG-synchronization. In these images coronary and aortic calcifications were identified automatically. Subsequently, the detected calcifications were quantified using coronary artery calcium Agatston and volume scores. Genotype data was available for these participants. A genome-wide association study was conducted on 10,220,814 SNPs using a linear regression model. To reduce multiple testing burden, known CAD/MI and BMD SNPs were specifically tested (45 SNPs from the CARDIoGRAMplusC4D consortium and 60 SNPS from the GEFOS consortium).No novel significant SNPs were found. Significant enrichment for CAD/MI SNPs was observed in testing Agatston and coronary artery calcium volume scores. Moreover, a significant enrichment of BMD SNPs was shown in aortic calcium volume scores. This may indicate genetic relation of BMD SNPs and arterial calcification burden.

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Large-scale image region documentation for fully automated image biomarker algorithm development and evaluation

2017

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, "Large-scale image region documentation for fully automated image biomarker algorithm development and evaluation," J. Med. Imag. 4(2), 024505 (2017), doi: 10.1117/1.JMI.4.2.024505. Abstract. With the advent of fully automated image analysis and modern machine learning methods, there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. This paper presents a method and implementation for facilitating such datasets that addresses the critical issue of size scaling for algorithm validation and evaluation; current evaluation methods that are usually used in academic studies do not scale to large datasets. This method includes protocols for the documentation of many regions in very large image datasets; the documentation may be incrementally updated by new image data and by improved algorithm outcomes. This method has been used for 5 years in the context of chest health biomarkers from low-dose chest CT images that are now being used with increasing frequency in lung cancer screening practice. The lung scans are segmented into over 100 different anatomical regions, and the method has been applied to a dataset of over 20,000 chest CT images. Using this framework, the computer algorithms have been developed to achieve over 90% acceptable image segmentation on the complete dataset. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Automated aortic calcification detection in low-dose chest CT images

Cited by 3 publications

References 8 publications

Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions

Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions

Genome-wide association study of coronary and aortic calcification in lung cancer screening CT

Large-scale image region documentation for fully automated image biomarker algorithm development and evaluation

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