Myocardial segmentation based on coronary anatomy using coronary computed tomography angiography: Development and validation in a pig model

Chung, Myung‐Sub; Yang, Dong Hyun; Kim, Young‐Hak; Kang, Soo–Jin; Jung, Joonho; Kim, Namkug; Heo, Seung‐Ho; Baek, Seunghee; Seo, Joon Beom; Choi, Byoung Wook; Kang, Joon‐Won; Lim, Tae‐Hwan

doi:10.1007/s00330-017-4793-0

Cited by 10 publications

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References 30 publications

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“…After extracting the centerline of each coronary artery and the left ventricular myocardium on the computed tomographic images, the 3D Voronoi algorithm was used to assign the myocardial territories of the major epicardial coronary arteries, including the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA). In brief, the Voronoi algorithm is a mathematical algorithm that divides the area or space between predetermined points or lines according to the shortest distances from those points or lines [ 18 – 20 ]. The left ventricular myocardial volume was divided into three major epicardial coronary artery territories based on the shortest distance from the coronary artery.…”

Section: Methodsmentioning

confidence: 99%

Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation

et al. 2018

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BackgroundInvasive fractional flow reserve (FFR) is a standard tool for identifying ischemia-producing coronary stenosis. However, in clinical practice, over 70% of treatment decisions still rely on visual estimation of angiographic stenosis, which has limited accuracy (about 60%–65%) for the prediction of FFR < 0.80. One of the reasons for the visual–functional mismatch is that myocardial ischemia can be affected by the supplied myocardial size, which is not always evident by coronary angiography. The aims of this study were to develop an angiography-based machine learning (ML) algorithm for predicting the supplied myocardial volume for a stenosis, as measured using coronary computed tomography angiography (CCTA), and then to build an angiography-based classifier for the lesions with an FFR < 0.80 versus ≥ 0.80.Methods and findingsA retrospective study was conducted using data from 1,132 stable and unstable angina patients with 1,132 intermediate lesions who underwent invasive coronary angiography, FFR, and CCTA at the Asan Medical Center, Seoul, Korea, between 1 May 2012 and 30 November 2015. The mean age was 63 ± 10 years, 76% were men, and 72% of the patients presented with stable angina. Of these, 932 patients (assessed before 31 January 2015) constituted the training set for the algorithm, and 200 patients (assessed after 1 February 2015) served as a test cohort to validate its diagnostic performance. Additionally, external validation with 79 patients from two centers (CHA University, Seongnam, Korea, and Ajou University, Suwon, Korea) was conducted. After automatic contour calibration using the caliber of guiding catheter, quantitative coronary angiography was performed using the edge-detection algorithms (CAAS-5, Pie-Medical). Clinical information was provided by the Asan BiomedicaL Research Environment (ABLE) system. The CCTA-based myocardial segmentation (CAMS)-derived myocardial volume supplied by each vessel (right coronary artery [RCA], left anterior descending [LAD], left circumflex [LCX]) and the myocardial volume subtended to a stenotic segment (CAMS-%Vsub) were measured for labeling. The ML for (1) predicting vessel territories (CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA) and CAMS-%Vsub and (2) identifying the lesions with an FFR < 0.80 was constructed. Angiography-based ML, employing a light gradient boosting machine (GBM), showed mean absolute errors (MAEs) of 5.42%, 8.57%, and 4.54% for predicting CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA, respectively. The percent myocardial volumes predicted by ML were used to predict the CAMS-%Vsub. With 5-fold cross validation, the MAEs between ML-predicted percent myocardial volume subtended to a stenotic segment (ML-%Vsub) and CAMS-%Vsub were minimized by the elastic net (6.26% ± 0.55% for LAD, 5.79% ± 0.68% for LCX, and 2.95% ± 0.14% for RCA lesions). Using all attributes (age, sex, involved vessel segment, and angiographic features affecting the myocardial territory and stenosis degree), the ML classifiers (L2 penalized logistic regression, support vector machine, ...

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Section: Methodsmentioning

confidence: 99%

Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation

et al. 2018

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“…In a per-segment analysis done by Ortiz-Perez et al 23% of the hyper enhanced regions on cardiac MRI were discordant with the empirically assigned coronary distribution as defined by the standard 17-segment model [ 17 ]. Use of Voronoi-based segmentation can overcome this problem as its accuracy has been reported in an animal study using swine hearts in which this method was found to be more accurate than the standard 17-segment model in predicting coronary territories [ 18 , 19 ].…”

Section: Discussionmentioning

confidence: 99%

Quantification of myocardial ischemia and subtended myocardial mass at adenosine stress cardiac computed tomography: a feasibility study

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et al. 2021

Int J Cardiovasc Imaging

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Combination of coronary computed tomography angiography (CCTA) and adenosine stress CT myocardial perfusion (CTP) allows for coronary artery lesion assessment as well as myocardial ischemia. However, myocardial ischemia on CTP is nowadays assessed semi-quantitatively by visual analysis. The aim of this study was to fully quantify myocardial ischemia and the subtended myocardial mass on CTP. We included 33 patients referred for a combined CCTA and adenosine stress CTP protocol, with good or excellent imaging quality on CTP. The coronary artery tree was automatically extracted from the CCTA and the relevant coronary artery lesions with a significant stenosis (≥ 50%) were manually defined using dedicated software. Secondly, epicardial and endocardial contours along with CT perfusion deficits were semi-automatically defined in short-axis reformatted images using MASS software. A Voronoi-based segmentation algorithm was used to quantify the subtended myocardial mass, distal from each relevant coronary artery lesion. Perfusion defect and subtended myocardial mass were spatially registered to the CTA. Finally, the subtended myocardial mass per lesion, total subtended myocardial mass and perfusion defect mass (per lesion) were measured. Voronoi-based segmentation was successful in all cases. We assessed a total of 64 relevant coronary artery lesions. Average values for left ventricular mass, total subtended mass and perfusion defect mass were 118, 69 and 7 g respectively. In 19/33 patients (58%) the total perfusion defect mass could be distributed over the relevant coronary artery lesion(s). Quantification of myocardial ischemia and subtended myocardial mass seem feasible at adenosine stress CTP and allows to quantitatively correlate coronary artery lesions to corresponding areas of myocardial hypoperfusion at CCTA and adenosine stress CTP.

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Vessel-specific coronary perfusion territories using a CT angiogram with a minimum cost path technique and its direct comparison to the American Heart Association 17-segment model

et al. 2020

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Myocardial segmentation based on coronary anatomy using coronary computed tomography angiography: Development and validation in a pig model

Abstract: • CAMS is a feasible method for identifying corresponding coronary territory using CTA • CAMS is more accurate in predicting coronary territory than the AHA method • The AHA method may underestimate the ischaemic territory of LAD stenosis.

Cited by 10 publications

References 30 publications

Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation

Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation

Quantification of myocardial ischemia and subtended myocardial mass at adenosine stress cardiac computed tomography: a feasibility study

Vessel-specific coronary perfusion territories using a CT angiogram with a minimum cost path technique and its direct comparison to the American Heart Association 17-segment model

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