2021
DOI: 10.1002/mp.15289
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Assessment of fully automatic segmentation of pulmonary artery and aorta on noncontrast CT with optimal surface graph cuts

Abstract: Purpose Accurate segmentation of the pulmonary arteries and aorta is important due to the association of the diameter and the shape of these vessels with several cardiovascular diseases and with the risk of exacerbations and death in patients with chronic obstructive pulmonary disease. We propose a fully automatic method based on an optimal surface graph‐cut algorithm to quantify the full 3D shape and the diameters of the pulmonary arteries and aorta in noncontrast computed tomography (CT) scans. Methods The p… Show more

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Cited by 6 publications
(8 citation statements)
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“…Improving the efficiency of image segmentation is the aim of many advanced computational studies that utilize machine learning and artificial intelligence tools. [33][34][35][36] Combining our FE workflow with automatic image segmentation would result in an improved clinical tool for assessing the risks of PPVI.…”
Section: Discussionmentioning
confidence: 99%
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“…Improving the efficiency of image segmentation is the aim of many advanced computational studies that utilize machine learning and artificial intelligence tools. [33][34][35][36] Combining our FE workflow with automatic image segmentation would result in an improved clinical tool for assessing the risks of PPVI.…”
Section: Discussionmentioning
confidence: 99%
“…While our models have significantly faster run times than previous studies, image segmentation and model generation are still relatively slow and tedious processes. Improving the efficiency of image segmentation is the aim of many advanced computational studies that utilize machine learning and artificial intelligence tools 33–36 . Combining our FE workflow with automatic image segmentation would result in an improved clinical tool for assessing the risks of PPVI.…”
Section: Discussionmentioning
confidence: 99%
“…However, few studies on pulmonary artery segmentation in CT images. [19][20][21][22][23][24] Linguraru et al 19 proposed a semiautomatic tool for analyzing lung CTA, using level sets and geodesic active contours to segment pulmonary arteries. Zhang et al 20 proposed a pulmonary artery segmentation and arterial tree slice tracking algorithm based on region growth and slice marching (RGSM) to segment the pulmonary artery.…”
Section: Introductionmentioning
confidence: 99%
“…In traditional methods, vascular segmentation has been reported in extensive literature in medical image research, and a wide range of technologies have been used, such as threshold segmentation, 12,13 region growth, 14,15 active contour, 16–18 etc. However, few studies on pulmonary artery segmentation in CT images 19–24 . Linguraru et al 19 .…”
Section: Introductionmentioning
confidence: 99%
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