Right ventricular endocardial segmentation in CMR images using a novel inter-modality statistical shape modelling approach

Concetta Piazzese; M. Chiara Carminati; Rolf Krause; Angelo Auricchio; Lynn Weinert; Gloria Tamborini; Mauro Pepi; Roberto M. Lang; Enrico G. Caiani

doi:10.22489/cinc.2016.026-356

Right ventricular endocardial segmentation in CMR images using a novel inter-modality statistical shape modelling approach

Concetta Piazzese^*, M. Chiara Carminati, Rolf Krause, Angelo Auricchio, 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

Abstract

Statistical shape modelling (SSM) approaches have been proposed as a powerful tool to segment the left ventricle in cardiac magnetic resonance (CMR) images. Our aim was to extend this method to segment the RV cavity in CMR images and validate it compared to the conventional gold-standard (GS) manual tracing. A SSM of the RV was built using a database of 4347 intrinsically 3D surfaces, extracted from transthoracic 3D echo cardiographic (3DE) images of 219 retrospective patients. The SSM was then scaled and deformed on the base of some features extracted, with different strategies, from each short-axis plane until a stable condition was reached. The proposed approach, tested on 14 patients, resulted in a high correlation (r²=0.97) and narrow limits of agreement (± 17% error) when comparing the semiautomatic volumes to the GS, confirming the accuracy of this approach in segmenting the RV endocardium.

Original language	English (US)
Title of host publication	Computing in Cardiology Conference, CinC 2016
Editors	Alan Murray
Publisher	IEEE Computer Society
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781509008964
DOIs	https://doi.org/10.22489/cinc.2016.026-356
State	Published - Mar 1 2016
Event	43rd Computing in Cardiology Conference, CinC 2016 - Vancouver, Canada Duration: Sep 11 2016 → Sep 14 2016

Publication series

Name	Computing in Cardiology
Volume	43
ISSN (Print)	2325-8861
ISSN (Electronic)	2325-887X

Conference

Conference	43rd Computing in Cardiology Conference, CinC 2016
Country/Territory	Canada
City	Vancouver
Period	09/11/16 → 09/14/16

Bibliographical note

Publisher Copyright:
© 2016 CCAL.

ASJC Scopus subject areas

General Computer Science
Cardiology and Cardiovascular Medicine

Access to Document

10.22489/cinc.2016.026-356

Cite this

Piazzese, C., Carminati, M. C., Krause, R., Auricchio, A., Weinert, L., Tamborini, G., Pepi, M., Lang, R. M., & Caiani, E. G. (2016). Right ventricular endocardial segmentation in CMR images using a novel inter-modality statistical shape modelling approach. In A. Murray (Ed.), Computing in Cardiology Conference, CinC 2016 (pp. 1-4). Article 7868685 (Computing in Cardiology; Vol. 43). IEEE Computer Society. https://doi.org/10.22489/cinc.2016.026-356

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title = "Right ventricular endocardial segmentation in CMR images using a novel inter-modality statistical shape modelling approach",

abstract = "Statistical shape modelling (SSM) approaches have been proposed as a powerful tool to segment the left ventricle in cardiac magnetic resonance (CMR) images. Our aim was to extend this method to segment the RV cavity in CMR images and validate it compared to the conventional gold-standard (GS) manual tracing. A SSM of the RV was built using a database of 4347 intrinsically 3D surfaces, extracted from transthoracic 3D echo cardiographic (3DE) images of 219 retrospective patients. The SSM was then scaled and deformed on the base of some features extracted, with different strategies, from each short-axis plane until a stable condition was reached. The proposed approach, tested on 14 patients, resulted in a high correlation (r2=0.97) and narrow limits of agreement (± 17% error) when comparing the semiautomatic volumes to the GS, confirming the accuracy of this approach in segmenting the RV endocardium.",

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

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doi = "10.22489/cinc.2016.026-356",

language = "English (US)",

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Piazzese, C, Carminati, MC, Krause, R, Auricchio, A, Weinert, L, Tamborini, G, Pepi, M, Lang, RM & Caiani, EG 2016, Right ventricular endocardial segmentation in CMR images using a novel inter-modality statistical shape modelling approach. in A Murray (ed.), Computing in Cardiology Conference, CinC 2016., 7868685, Computing in Cardiology, vol. 43, IEEE Computer Society, pp. 1-4, 43rd Computing in Cardiology Conference, CinC 2016, Vancouver, Canada, 09/11/16. https://doi.org/10.22489/cinc.2016.026-356

Right ventricular endocardial segmentation in CMR images using a novel inter-modality statistical shape modelling approach. / Piazzese, Concetta; Carminati, M. Chiara; Krause, Rolf et al.
Computing in Cardiology Conference, CinC 2016. ed. / Alan Murray. IEEE Computer Society, 2016. p. 1-4 7868685 (Computing in Cardiology; Vol. 43).

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

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AU - Weinert, Lynn

AU - Tamborini, Gloria

AU - Pepi, Mauro

AU - Lang, Roberto M.

AU - Caiani, Enrico G.

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N2 - Statistical shape modelling (SSM) approaches have been proposed as a powerful tool to segment the left ventricle in cardiac magnetic resonance (CMR) images. Our aim was to extend this method to segment the RV cavity in CMR images and validate it compared to the conventional gold-standard (GS) manual tracing. A SSM of the RV was built using a database of 4347 intrinsically 3D surfaces, extracted from transthoracic 3D echo cardiographic (3DE) images of 219 retrospective patients. The SSM was then scaled and deformed on the base of some features extracted, with different strategies, from each short-axis plane until a stable condition was reached. The proposed approach, tested on 14 patients, resulted in a high correlation (r2=0.97) and narrow limits of agreement (± 17% error) when comparing the semiautomatic volumes to the GS, confirming the accuracy of this approach in segmenting the RV endocardium.

AB - Statistical shape modelling (SSM) approaches have been proposed as a powerful tool to segment the left ventricle in cardiac magnetic resonance (CMR) images. Our aim was to extend this method to segment the RV cavity in CMR images and validate it compared to the conventional gold-standard (GS) manual tracing. A SSM of the RV was built using a database of 4347 intrinsically 3D surfaces, extracted from transthoracic 3D echo cardiographic (3DE) images of 219 retrospective patients. The SSM was then scaled and deformed on the base of some features extracted, with different strategies, from each short-axis plane until a stable condition was reached. The proposed approach, tested on 14 patients, resulted in a high correlation (r2=0.97) and narrow limits of agreement (± 17% error) when comparing the semiautomatic volumes to the GS, confirming the accuracy of this approach in segmenting the RV endocardium.

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Piazzese C, Carminati MC, Krause R, Auricchio A, Weinert L, Tamborini G et al. Right ventricular endocardial segmentation in CMR images using a novel inter-modality statistical shape modelling approach. In Murray A, editor, Computing in Cardiology Conference, CinC 2016. IEEE Computer Society. 2016. p. 1-4. 7868685. (Computing in Cardiology). doi: 10.22489/cinc.2016.026-356

Right ventricular endocardial segmentation in CMR images using a novel inter-modality statistical shape modelling approach

Abstract

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Bibliographical note

ASJC Scopus subject areas

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