1 April 2016 Automatic left-atrial segmentation from cardiac 3D ultrasound: a dual-chamber model-based approach
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Abstract
In this paper, we present an automatic solution for segmentation and quantification of the left atrium (LA) from 3D cardiac ultrasound. A model-based framework is applied, making use of (deformable) active surfaces to model the endocardial surfaces of cardiac chambers, allowing incorporation of a priori anatomical information in a simple fashion. A dual-chamber model (LA and left ventricle) is used to detect and track the atrio-ventricular (AV) plane, without any user input. Both chambers are represented by parametric surfaces and a Kalman filter is used to fit the model to the position of the endocardial walls detected in the image, providing accurate detection and tracking during the whole cardiac cycle. This framework was tested in 20 transthoracic cardiac ultrasound volumetric recordings of healthy volunteers, and evaluated using manual traces of a clinical expert as a reference. The 3D meshes obtained with the automatic method were close to the reference contours at all cardiac phases (mean distance of 0.03±0.6 mm). The AV plane was detected with an accuracy of −0.6±1.0 mm. The LA volumes assessed automatically were also in agreement with the reference (mean ±1.96 SD): 0.4±5.3 ml, 2.1±12.6 ml, and 1.5±7.8 ml at end-diastolic, end-systolic and pre-atrial-contraction frames, respectively. This study shows that the proposed method can be used for automatic volumetric assessment of the LA, considerably reducing the analysis time and effort when compared to manual analysis.
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Nuno Almeida, Nuno Almeida, Sebastian I. Sarvari, Sebastian I. Sarvari, Fredrik Orderud, Fredrik Orderud, Olivier Gérard, Olivier Gérard, Jan D'hooge, Jan D'hooge, Eigil Samset, Eigil Samset, "Automatic left-atrial segmentation from cardiac 3D ultrasound: a dual-chamber model-based approach", Proc. SPIE 9790, Medical Imaging 2016: Ultrasonic Imaging and Tomography, 97900D (1 April 2016); doi: 10.1117/12.2216666; https://doi.org/10.1117/12.2216666
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