A robust coronary artery identification and centerline extraction method in angiographies

Li, Zhixun; Zhang, Yingtao; Liu, Guangzhong; Shao, Haoyang; Li, Weimin; Tang, Xianglong

doi:10.1016/j.bspc.2014.09.015

Cited by 36 publications

(22 citation statements)

References 18 publications

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“…The method is based on examination of a Hessian matrix and corresponding eigenvalues for the extraction of tubular objects. After eigenvalue decomposition of the Hessian matrix, the eigenvalues λ 1, λ 2 , and λ 3 are sorted as |λ 1 3 | are the conditions for an ideal tubular structure in a 3D image. On the basis of this analysis, Frangi's vesselness measure distinguishes tube-like objects from blob-like or plate-like structures and the background.…”

Section: Hessian-based Multiscale Vessel Enhancementmentioning

confidence: 99%

Robust seed detection for coronary arteries segmentation using thresholded Frangi response

Zai

Ansari

Sang-Hwa³

et al. 2017

Turk J Elec Eng & Comp Sci

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Automatic detection of initial seed points has become an essential step towards delineating coronary arteries in coronary computed tomography angiography (CCTA) images due to image inhomogeneity and other factors. Most coronary segmentation algorithms require user interaction for seed point selection, which may lead to erroneous segmentation. In this study, we present an improved technique of seed detection for coronary segmentation using a thresholded Frangi response. Before computing region of interest (ROI), the proposed method first computes the Frangi response of the complete CCTA volume, followed by thresholding with respect to quantile and median values, and then the ROI selection procedure is applied. Further, this procedure is joined with a feature that is built according to the resemblance among consecutive orthogonal cross-sections. The proposed method was evaluated on nine clinical datasets, and the proposed framework automatically detected coronary seeds accurately and can be used for an accurate delineation of coronary arteries. The obtained results were compared qualitatively and quantitatively by a radiologist, and the proposed method outperformed the previous method with an improvement of 45.9%.

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Section: Hessian-based Multiscale Vessel Enhancementmentioning

confidence: 99%

Robust seed detection for coronary arteries segmentation using thresholded Frangi response

Zai

Ansari

Sang-Hwa³

et al. 2017

Turk J Elec Eng & Comp Sci

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“…Due to the similarity in angiography images, fine-tuned parameters will slightly change their results. Its details are described in [28].…”

Section: Parameter Settingmentioning

confidence: 99%

“…The proposed method initiates from a vessel centerline, which can be obtained from an existing fully-automatic, semi-automatic or manual technology. In this work, a fully automatic procedure is established based on previous findings on centerline extraction [28].…”

Section: Introductionmentioning

confidence: 99%

Automatic coronary artery segmentation based on multi-domains remapping and quantile regression in angiographies

Zhang

Gong

et al. 2016

Computerized Medical Imaging and Graphics

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Coronary artery disease has become the most dangerous diseases to human life. And coronary artery segmentation is the basis of computer aided diagnosis and analysis. Existing segmentation methods are difficult to handle the complex vascular texture due to the projective nature in conventional coronary angiography. Due to large amount of data and complex vascular shapes, any manual annotation has become increasingly unrealistic. A fully automatic segmentation method is necessary in clinic practice. In this work, we study a method based on reliable boundaries via multi-domains remapping and robust discrepancy correction via distance balance and quantile regression for automatic coronary artery segmentation of angiography images. The proposed method can not only segment overlapping vascular structures robustly, but also achieve good performance in low contrast regions. The effectiveness of our approach is demonstrated on a variety of coronary blood vessels compared with the existing methods. The overall segmentation performances si, fnvf, fvpf and tpvf were 95.135%, 3.733%, 6.113%, 96.268%, respectively.

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“…Coronary heart disease (CHD) has become the leading cause of death worldwide [1][2]. Coronary angiography (CAG) is the gold standard in the diagnosis of CHD [3]. Although the use of hypotonic non-ionic contrast agents can reduce renal damage, contrast-induced nephropathy (CIN) has significantly increased in recent years because of CAG being widely promoted.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Using eGFR to Diagnose Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography

Gao¹,

Huang²,

Geng³

et al. 2020

JICOA

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Background: Contrast-induced nephropathy (CIN) is one of the common complications of coronary angiography (CAG). The changes of serum creatinine (Scr) before and after angiography were used to diagnose CIN in the world. But Scr is not a sensitive index to reflect early renal dysfunction. Estimated glomerular filtration rate (eGFR) is a comprehensive indicator to evaluate renal function but does not have accepted standard to diagnose CIN till now. This study aimed to investigate the feasibility of using eGFR to diagnose CIN in patients undergoing CAG. Methods: We included 300 coronary heart disease (CHD) patients who underwent CAG. Their demographics and basal renal function were recorded. Changes of Scr and eGFR before and after CAG were compared at the same time. Logistic regression was used to find independent influence factors of CIN. Receptor operating characteristic (ROC) curve was used to find the optimum cut-off value of eGFR for diagnosing CIN. Results: Among 300 patients with CHD, 64 (41 males and 23 females) of them were affected by CIN after CAG, with a total incidence of 21.3%. Among 271 patients whose Scr were normal (< 133 μmol/L) before CAG, 109 (40.2%) of them with impaired eGFR (< 90 ml/min/1.73m²). Patients had normal eGFR before CAG were less likely to develop CIN than those with normal Scr (15.4% vs 20.7%, P < 0.05). Logistic regression analysis showed that men, diabetes, multivessel lesion and eGFR were independent factors of CIN. ROC curve showed that the optimum cut-off value for diagnosing CIN was eGFR decrease by 22.5% after CAG (sensitivity = 98.4%, specificity = 98.3%, AUC = 0.973, 95%CI: 0.942-1.000, P = 0.000). Conclusions: eGFR is an independent factor of CIN, which is more sensitive than Scr in reflecting early renal dysfunction. Using eGFR to diagnose CIN is feasible in the clinic, but the cut-off value still needs to be confirmed by large scale clinical trials.

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A robust coronary artery identification and centerline extraction method in angiographies

Cited by 36 publications

References 18 publications

Robust seed detection for coronary arteries segmentation using thresholded Frangi response

Robust seed detection for coronary arteries segmentation using thresholded Frangi response

Automatic coronary artery segmentation based on multi-domains remapping and quantile regression in angiographies

Feasibility of Using eGFR to Diagnose Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography

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