3-D Quantitative Vascular Shape Analysis for Arterial Bifurcations via Dynamic Tube Fitting

Wu, Yin; Liatsis, Panos

doi:10.1109/tbme.2011.2179654

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…The proposed algorithm is adaptive to the vasculature complexity and robust to strongly bended lumen as well as branching vasculature. By registering an elliptical cross-sectional tube with the desired constituent vessel in every major bifurcation of the arterial tree, Wang and Liatsis [202] proposed a deformable tube model-based technique for precise estimation of the centerline and reference lumen surface for both the main vessel and the side branches in the area of bifurcations. Meanwhile, a completely automatic method based on graph-cuts was designed for the accurate extraction of coronary centerline in X-ray angiography imagery [203] .…”

Section: Computer Vision Techniques In Transcatheter Interventionmentioning

confidence: 99%

Computer Vision Techniques for Transcatheter Intervention

Zhang

Xie

Roach

2015

IEEE J. Transl. Eng. Health Med.

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Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and the treatment of cardiovascular diseases. For example, transcatheter aortic valve implantation is an alternative to aortic valve replacement for the treatment of severe aortic stenosis, and transcatheter atrial fibrillation ablation is widely used for the treatment and the cure of atrial fibrillation. In addition, catheter-based intravascular ultrasound and optical coherence tomography imaging of coronary arteries provides important information about the coronary lumen, wall, and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial to the evaluation and the treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation and motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods. We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence, it is important to understand the application domain, clinical background, and imaging modality, so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on the background information of the transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area.

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Section: Computer Vision Techniques In Transcatheter Interventionmentioning

confidence: 99%

Computer Vision Techniques for Transcatheter Intervention

Zhang

Xie

Roach

2015

IEEE J. Transl. Eng. Health Med.

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Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis During CCTA Evaluation of Chest Pain in the Emergency Department: Preparing an Application for Real-world Use

et al. 2021

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Coronary Computed Tomography Angiography (CCTA) evaluation of chest-pain patients in an Emergency Department (ED) is considered appropriate. While a "negative" CCTA interpretation supports direct patient discharge from an ED, labor-intensive analyses are required, with accuracy in jeopardy from distractions. We describe the development of an Artificial Intelligence (AI) algorithm and workflow for assisting interpreting physicians in CCTA screening for absence of coronary atherosclerosis. The two-phase approach consisted of: (1) Phase 1 -focused on development and preliminary Testing of an algorithm for vesselcenterline extraction classification in a balanced study population (n = 500 with 50% disease prevalence) derived by retrospective random case selection; and (2) Phase 2 -concerned with simulated-clinical Trialing of developed algorithm on a per-case basis in a more "real-world" study population (n = 100 with 28% disease prevalence) from an ED chest-pain series. This allowed pre-deployment evaluation of the AI-based CCTA screening application which provides vessel-by-vessel graphic display of algorithm inference results integrated into a clinically capable viewer. Algorithm performance evaluation used Area Under the Receiver-Operating-Characteristic Curve (AUC-ROC); confusion matrices reflected ground-truth vs AI determinations. The vessel-based algorithm demonstrated strong performance with AUC-ROC = 0.96. In both Phase 1 and Phase 2, independent of disease prevalence differences, negative predictive values at the case level were very high at 95%. The rate of completion of the algorithm workflow process (96% with inference results in 55-80 seconds) in Phase 2 depended on adequate image quality.There is potential for this AI application to assist in CCTA interpretation to help extricate atherosclerosis from chest-pain presentations.

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3-D Vascular Skeleton Extraction and Decomposition

Chowriappa

Seo

Salunke

et al. 2014

IEEE J. Biomed. Health Inform.

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We introduce a novel vascular skeleton extraction and decomposition technique for computer-assisted diagnosis and analysis. We start by addressing the problem of vascular decomposition as a cluster optimization problem and present a methodology for weighted convex approximations. Decomposed vessel structures are then grouped using the vessel skeleton, extracted using a Laplace-based operator. The method is validated using presegmented sections of vasculature archived for 98 aneurysms in 112 patients. We test first for vascular decomposition and next for vessel skeleton extraction. The proposed method produced promising results with an estimated 80.5% of the vessel sections correctly decomposed and 92.9% of the vessel sections having the correct number of skeletal branches, identified by a clinical radiological expert. Next, the method was validated on longitudinal study data from n = 4 subjects, where vascular skeleton extraction and decomposition was performed. Volumetric and surface area comparisons were made between expert segmented sections and the proposed approach on sections containing aneurysms. Results suggest that the method is able to detect changes in aneurysm volumes and surface areas close to that segmented by an expert.

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3-D Quantitative Vascular Shape Analysis for Arterial Bifurcations via Dynamic Tube Fitting

Abstract: This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link

Cited by 5 publications

References 22 publications

Computer Vision Techniques for Transcatheter Intervention

Computer Vision Techniques for Transcatheter Intervention

Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis During CCTA Evaluation of Chest Pain in the Emergency Department: Preparing an Application for Real-world Use

3-D Vascular Skeleton Extraction and Decomposition

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