Quantitative assessment of mitral valve (MV) morphology is important for diagnosing MV pathology and for planning of reparative procedures. Although this is typically done using 3D transesophageal echocardiography (TEE), recent advances in the spatiotemporal resolution of 3D transthoracic echocardiography (TTE) have enabled the use of this more patient friendly modality. However, manual data analysis is time consuming and operator dependent. In this study, a fully automatic method for MV segmentation and tracking in 3D TTE is proposed and validated. The proposed framework takes advantage of a previously proposed left ventricle (LV) segmentation framework to localize the MV and performs segmentation based on the B-spline Explicit Active Surfaces (BEAS) framework. The orientation of the MV is obtained and the MV surface is cropped to the mitral annulus (MA) and divided into posterior and anterior leaflets. The segmented MV at end diastole (ED) is propagated to end systole (ES) using localized anatomical affine optical flow (lAAOF). Because the orientation and leaflet division is known, relevant clinical parameters can then be extracted from the mesh at any time point. The proposed framework shows excellent segmentation results with a mean absolute distance (MAD) and Hausdorff distance (HD) of 1.19+0.25 ext{mm} and 5.79pm 1.25 ext{mm} at ED and 1.39pm 0.32 ext{mm} and 6.70pm 1.97 ext{mm} at ES against manual analysis. In conclusion, an automatic method for MV segmentation is proposed which could provide valuable clinical information in a more patient-friendly manner.

Pedrosa, J., Queiros, S., Vilaca, J., Badano, L., D'Hooge, J. (2018). Fully Automatic Assessment of Mitral Valve Morphology from 3D Transthoracic Echocardiography. In IEEE International Ultrasonics Symposium, IUS (pp.1-6). IEEE Computer Society [10.1109/ULTSYM.2018.8580112].

Fully Automatic Assessment of Mitral Valve Morphology from 3D Transthoracic Echocardiography

Badano L.;
2018

Abstract

Quantitative assessment of mitral valve (MV) morphology is important for diagnosing MV pathology and for planning of reparative procedures. Although this is typically done using 3D transesophageal echocardiography (TEE), recent advances in the spatiotemporal resolution of 3D transthoracic echocardiography (TTE) have enabled the use of this more patient friendly modality. However, manual data analysis is time consuming and operator dependent. In this study, a fully automatic method for MV segmentation and tracking in 3D TTE is proposed and validated. The proposed framework takes advantage of a previously proposed left ventricle (LV) segmentation framework to localize the MV and performs segmentation based on the B-spline Explicit Active Surfaces (BEAS) framework. The orientation of the MV is obtained and the MV surface is cropped to the mitral annulus (MA) and divided into posterior and anterior leaflets. The segmented MV at end diastole (ED) is propagated to end systole (ES) using localized anatomical affine optical flow (lAAOF). Because the orientation and leaflet division is known, relevant clinical parameters can then be extracted from the mesh at any time point. The proposed framework shows excellent segmentation results with a mean absolute distance (MAD) and Hausdorff distance (HD) of 1.19+0.25 ext{mm} and 5.79pm 1.25 ext{mm} at ED and 1.39pm 0.32 ext{mm} and 6.70pm 1.97 ext{mm} at ES against manual analysis. In conclusion, an automatic method for MV segmentation is proposed which could provide valuable clinical information in a more patient-friendly manner.
paper
B-spline explicit active surfaces; mitral valve; real-time 3D echocardiography; segmentation
English
2018 IEEE International Ultrasonics Symposium, IUS 2018 22-25 Oct
2018
IEEE International Ultrasonics Symposium, IUS
978-1-5386-3425-7
2018
2018-
1
6
8580112
none
Pedrosa, J., Queiros, S., Vilaca, J., Badano, L., D'Hooge, J. (2018). Fully Automatic Assessment of Mitral Valve Morphology from 3D Transthoracic Echocardiography. In IEEE International Ultrasonics Symposium, IUS (pp.1-6). IEEE Computer Society [10.1109/ULTSYM.2018.8580112].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/279489
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