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Proceedings Paper

Aortic root segmentation in 4D transesophageal echocardiography
Author(s): Shubham Chechani; Rahul Suresh; Kedar A. Patwardhan
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Paper Abstract

The Aortic Valve (AV) is an important anatomical structure which lies on the left side of the human heart. The AV regulates the flow of oxygenated blood from the Left Ventricle (LV) to the rest of the body through aorta. Pathologies associated with the AV manifest themselves in structural and functional abnormalities of the valve. Clinical management of pathologies often requires repair, reconstruction or even replacement of the valve through surgical intervention. Assessment of these pathologies as well as determination of specific intervention procedure requires quantitative evaluation of the valvular anatomy. 4D (3D + t) Transesophageal Echocardiography (TEE) is a widely used imaging technique that clinicians use for quantitative assessment of cardiac structures. However, manual quantification of 3D structures is complex, time consuming and suffers from inter-observer variability. Towards this goal, we present a semiautomated approach for segmentation of the aortic root (AR) structure. Our approach requires user-initialized landmarks in two reference frames to provide AR segmentation for full cardiac cycle. We use ‘coarse-to-fine’ B-spline Explicit Active Surface (BEAS) for AR segmentation and Masked Normalized Cross Correlation (NCC) method for AR tracking. Our method results in approximately 0.51 mm average localization error in comparison with ground truth annotation performed by clinical experts on 10 real patient cases (139 3D volumes).

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105750W (27 February 2018); doi: 10.1117/12.2293056
Show Author Affiliations
Shubham Chechani, Samsung R&D Institute India - Bangalore (India)
Rahul Suresh, Samsung R&D Institute India - Bangalore (India)
Kedar A. Patwardhan, Samsung R&D Institute India - Bangalore (India)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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