Evaluation of different statistical shape models for segmentation of the left ventricular endocardium from magnetic resonance images

Concetta Piazzese; M. Chiara Carminati; Andrea Colombo; Rolf Krause; Mark Potse; Lynn Weinert; Gloria Tamborini; Mauro Pepi; Roberto M. Lang; Enrico G. Caiani

doi:10.1109/CIC.2015.7408597

Evaluation of different statistical shape models for segmentation of the left ventricular endocardium from magnetic resonance images

Concetta Piazzese^*, M. Chiara Carminati, Andrea Colombo, Rolf Krause, Mark Potse, Lynn Weinert, Gloria Tamborini, Mauro Pepi, Roberto M. Lang, Enrico G. Caiani

^*Corresponding author for this work

Applied Mathematics and Computational Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Statistical shape models (SSMs) represent a powerful tool used in patient-specific modeling to segment medical images because they incorporate a-priori knowledge that guide the model during deformation. Our aim was to evaluate segmentation accuracy in terms of left ventricular (LV) volumes obtained using four different SSMs versus manual gold standard tracing on cardiac magnetic resonance (CMR) images. A database of 3D echocardiographic (3DE) LV surfaces obtained in 435 patients was used to generate four different SSMs, based on cardiac phase selection. Each model was scaled and deformed to detect LV endocardial contours in the end-diastolic (ED) and end-systolic (ES) frames of a CMR short-axis (SAX) stack for 15 patients with normal LV function. Linear correlation and Bland-Altman analyses versus gold-standard showed in all cases high correlation (r2>0.95), non-significant biases and narrow limits of agreement.

Original language	English (US)
Title of host publication	Computing in Cardiology Conference 2015, CinC 2015
Editors	Alan Murray
Publisher	IEEE Computer Society
Pages	105-108
Number of pages	4
ISBN (Electronic)	9781509006854
DOIs	https://doi.org/10.1109/CIC.2015.7408597
State	Published - Feb 16 2015
Event	42nd Computing in Cardiology Conference, CinC 2015 - Nice, France Duration: Sep 6 2015 → Sep 9 2015

Publication series

Name	Computing in Cardiology
Volume	42
ISSN (Print)	2325-8861
ISSN (Electronic)	2325-887X

Conference

Conference	42nd Computing in Cardiology Conference, CinC 2015
Country/Territory	France
City	Nice
Period	09/6/15 → 09/9/15

Bibliographical note

Publisher Copyright:
© 2015 CCAL.

ASJC Scopus subject areas

General Computer Science
Cardiology and Cardiovascular Medicine

Access to Document

10.1109/CIC.2015.7408597

Cite this

Piazzese, C., Carminati, M. C., Colombo, A., Krause, R., Potse, M., Weinert, L., Tamborini, G., Pepi, M., Lang, R. M., & Caiani, E. G. (2015). Evaluation of different statistical shape models for segmentation of the left ventricular endocardium from magnetic resonance images. In A. Murray (Ed.), Computing in Cardiology Conference 2015, CinC 2015 (pp. 105-108). Article 7408597 (Computing in Cardiology; Vol. 42). IEEE Computer Society. https://doi.org/10.1109/CIC.2015.7408597

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title = "Evaluation of different statistical shape models for segmentation of the left ventricular endocardium from magnetic resonance images",

abstract = "Statistical shape models (SSMs) represent a powerful tool used in patient-specific modeling to segment medical images because they incorporate a-priori knowledge that guide the model during deformation. Our aim was to evaluate segmentation accuracy in terms of left ventricular (LV) volumes obtained using four different SSMs versus manual gold standard tracing on cardiac magnetic resonance (CMR) images. A database of 3D echocardiographic (3DE) LV surfaces obtained in 435 patients was used to generate four different SSMs, based on cardiac phase selection. Each model was scaled and deformed to detect LV endocardial contours in the end-diastolic (ED) and end-systolic (ES) frames of a CMR short-axis (SAX) stack for 15 patients with normal LV function. Linear correlation and Bland-Altman analyses versus gold-standard showed in all cases high correlation (r2>0.95), non-significant biases and narrow limits of agreement.",

author = "Concetta Piazzese and Carminati, {M. Chiara} and Andrea Colombo and Rolf Krause and Mark Potse and Lynn Weinert and Gloria Tamborini and Mauro Pepi and Lang, {Roberto M.} and Caiani, {Enrico G.}",

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doi = "10.1109/CIC.2015.7408597",

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Piazzese, C, Carminati, MC, Colombo, A, Krause, R, Potse, M, Weinert, L, Tamborini, G, Pepi, M, Lang, RM & Caiani, EG 2015, Evaluation of different statistical shape models for segmentation of the left ventricular endocardium from magnetic resonance images. in A Murray (ed.), Computing in Cardiology Conference 2015, CinC 2015., 7408597, Computing in Cardiology, vol. 42, IEEE Computer Society, pp. 105-108, 42nd Computing in Cardiology Conference, CinC 2015, Nice, France, 09/6/15. https://doi.org/10.1109/CIC.2015.7408597

Evaluation of different statistical shape models for segmentation of the left ventricular endocardium from magnetic resonance images. / Piazzese, Concetta; Carminati, M. Chiara; Colombo, Andrea et al.
Computing in Cardiology Conference 2015, CinC 2015. ed. / Alan Murray. IEEE Computer Society, 2015. p. 105-108 7408597 (Computing in Cardiology; Vol. 42).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Potse, Mark

AU - Weinert, Lynn

AU - Tamborini, Gloria

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AB - Statistical shape models (SSMs) represent a powerful tool used in patient-specific modeling to segment medical images because they incorporate a-priori knowledge that guide the model during deformation. Our aim was to evaluate segmentation accuracy in terms of left ventricular (LV) volumes obtained using four different SSMs versus manual gold standard tracing on cardiac magnetic resonance (CMR) images. A database of 3D echocardiographic (3DE) LV surfaces obtained in 435 patients was used to generate four different SSMs, based on cardiac phase selection. Each model was scaled and deformed to detect LV endocardial contours in the end-diastolic (ED) and end-systolic (ES) frames of a CMR short-axis (SAX) stack for 15 patients with normal LV function. Linear correlation and Bland-Altman analyses versus gold-standard showed in all cases high correlation (r2>0.95), non-significant biases and narrow limits of agreement.

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Piazzese C, Carminati MC, Colombo A, Krause R, Potse M, Weinert L et al. Evaluation of different statistical shape models for segmentation of the left ventricular endocardium from magnetic resonance images. In Murray A, editor, Computing in Cardiology Conference 2015, CinC 2015. IEEE Computer Society. 2015. p. 105-108. 7408597. (Computing in Cardiology). doi: 10.1109/CIC.2015.7408597

Evaluation of different statistical shape models for segmentation of the left ventricular endocardium from magnetic resonance images

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ASJC Scopus subject areas

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