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A statistical shape model of the left ventricle from real-time 3D echocardiography and its application to myocardial segmentation of cardiac magnetic resonance images

Carminati, M.C., Piazzese, C., Pepi, M., Tamborini, G., Gripari, P., Pontone, G., Krause, R., Auricchio, A., Lang, R.M. and Caiani, E.G. 2018. A statistical shape model of the left ventricle from real-time 3D echocardiography and its application to myocardial segmentation of cardiac magnetic resonance images. Computers in Biology and Medicine 96 , pp. 241-251. 10.1016/j.compbiomed.2018.03.013

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Abstract

Object We present in this paper the application of a statistical shape model of the left ventricle (LV) built from transthoracic real time 3D echocardiography (3DE) to segment the LV endocardium and epicardium in cardiac magnetic resonance (CMR) images. Material and methods The LV model was built from a training database constituted by over 9000 surfaces obtained from retrospectively selected 3DE examination of 435 patients with various pathologies. Three-dimensional segmentation of the endocardium and the epicardium was obtained by processing CMR images acquired in 30 patients with a dedicated active shape modelling (ASM) algorithm using the proposed LV model. Results The segmentation results obtained with the proposed method were compared with those obtained by the manual reference technique; similarity was proven by computing: i) point to surface distance (<2 mm), ii) Dice similarity coefficient (>89%), iii) Hausdorff distance ( ∼ 5 mm). This was furthermore confirmed by equivalence testing, linear regression and Bland Altman analysis applied on derived clinical parameters, such as LV volumes and mass. Conclusions This study showed the potential usefulness of the proposed inter-modal ASM approach featuring a 3DE-based LV model for the 3D segmentation of the LV myocardium in CMR images.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0010-4825
Date of Acceptance: 21 March 2018
Last Modified: 16 Jun 2019 18:11
URI: http://orca-mwe.cf.ac.uk/id/eprint/120445

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