Semi-automated Detection and Quantification of Aortic Atheromas from Three-Dimensional Transesophageal Echocardiography

Concetta Piazzese; Wendy Tsang; Miguel Sotaquira; Roberto M. Lang; Enrico G. Caiani

Semi-automated Detection and Quantification of Aortic Atheromas from Three-Dimensional Transesophageal Echocardiography

Concetta Piazzese, Wendy Tsang, Miguel Sotaquira, Roberto M. Lang, Enrico G. Caiani

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

Abstract

Quantification of descending aorta atheromas (AT) with 2D transesophageal echocardiography (TEE) is time-consuming and underestimates plaque burden, while 3D TEE can provide the basis for analysis of AT 3D dimensions and volume. We developed a novel semi-automated algorithm to detect aortic AT in 3D TEE, to quantify their severity and volume and tested its accuracy compared to an expert gold standard. The 3D TEE datasets acquired from 58 consecutive patients were analyzed. Results showed a good accuracy in comparison with expert analysis, with an agreement of 95% in absolute presence/absence of AT per patient, 89% in AT number and location and 85% in patient risk due to AT severity classification. AT volumes were highly correlated (R²=0.98), with a slight underestimation (9%) compared to manual measurements. The proposed semi-automated algorithm is rapid, feasible and accurate in analyzing AT from 3D TEE descending aorta datasets.

Original language	English
Title of host publication	Computing in Cardiology 2013, CinC 2013
Editors	Alan Murray
Publisher	IEEE Computer Society
Pages	13-16
Number of pages	4
Volume	40
ISBN (Electronic)	9781479908868
ISBN (Print)	9781479908844
Publication status	Published - 2013
Externally published	Yes
Event	40th Computing in Cardiology Conference - Zaragoza, Spain Duration: 22 Sept 2013 → 25 Sept 2013 Conference number: 40 https://www.cinc.org/past-computing-in-cardiology-conferences/ https://i3a.unizar.es/cinc2013/

Publication series

Name	Computing in Cardiology
Volume	40
ISSN (Print)	2325-8861
ISSN (Electronic)	2325-887X

Conference

Conference	40th Computing in Cardiology Conference
Abbreviated title	CinC 2013
Country/Territory	Spain
City	Zaragoza
Period	22/09/13 → 25/09/13
Internet address	https://www.cinc.org/past-computing-in-cardiology-conferences/ https://i3a.unizar.es/cinc2013/

Access to Document

https://ieeexplore.ieee.org/document/6712399Licence: CC BY

Cite this

Piazzese, C., Tsang, W., Sotaquira, M., Lang, R. M., & Caiani, E. G. (2013). Semi-automated Detection and Quantification of Aortic Atheromas from Three-Dimensional Transesophageal Echocardiography. In A. Murray (Ed.), Computing in Cardiology 2013, CinC 2013 (Vol. 40, pp. 13-16). Article 6712399 (Computing in Cardiology; Vol. 40). IEEE Computer Society. https://ieeexplore.ieee.org/document/6712399

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title = "Semi-automated Detection and Quantification of Aortic Atheromas from Three-Dimensional Transesophageal Echocardiography",

abstract = "Quantification of descending aorta atheromas (AT) with 2D transesophageal echocardiography (TEE) is time-consuming and underestimates plaque burden, while 3D TEE can provide the basis for analysis of AT 3D dimensions and volume. We developed a novel semi-automated algorithm to detect aortic AT in 3D TEE, to quantify their severity and volume and tested its accuracy compared to an expert gold standard. The 3D TEE datasets acquired from 58 consecutive patients were analyzed. Results showed a good accuracy in comparison with expert analysis, with an agreement of 95\% in absolute presence/absence of AT per patient, 89\% in AT number and location and 85\% in patient risk due to AT severity classification. AT volumes were highly correlated (R2=0.98), with a slight underestimation (9\%) compared to manual measurements. The proposed semi-automated algorithm is rapid, feasible and accurate in analyzing AT from 3D TEE descending aorta datasets.",

keywords = "Three-dimensional displays, Abstracts, Computers",

author = "Concetta Piazzese and Wendy Tsang and Miguel Sotaquira and Lang, \{Roberto M.\} and Caiani, \{Enrico G.\}",

year = "2013",

language = "English",

isbn = "9781479908844",

volume = "40",

series = "Computing in Cardiology",

publisher = "IEEE Computer Society",

pages = "13--16",

editor = "Alan Murray",

booktitle = "Computing in Cardiology 2013, CinC 2013",

address = "United States",

note = "40th Computing in Cardiology Conference, CinC 2013 ; Conference date: 22-09-2013 Through 25-09-2013",

url = "https://www.cinc.org/past-computing-in-cardiology-conferences/, https://i3a.unizar.es/cinc2013/",

}

Piazzese, C, Tsang, W, Sotaquira, M, Lang, RM & Caiani, EG 2013, Semi-automated Detection and Quantification of Aortic Atheromas from Three-Dimensional Transesophageal Echocardiography. in A Murray (ed.), Computing in Cardiology 2013, CinC 2013. vol. 40, 6712399, Computing in Cardiology, vol. 40, IEEE Computer Society, pp. 13-16, 40th Computing in Cardiology Conference, Zaragoza, Spain, 22/09/13. <https://ieeexplore.ieee.org/document/6712399>

Semi-automated Detection and Quantification of Aortic Atheromas from Three-Dimensional Transesophageal Echocardiography. / Piazzese, Concetta; Tsang, Wendy; Sotaquira, Miguel et al.
Computing in Cardiology 2013, CinC 2013. ed. / Alan Murray. Vol. 40 IEEE Computer Society, 2013. p. 13-16 6712399 (Computing in Cardiology; Vol. 40).

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

TY - GEN

T1 - Semi-automated Detection and Quantification of Aortic Atheromas from Three-Dimensional Transesophageal Echocardiography

AU - Piazzese, Concetta

AU - Tsang, Wendy

AU - Sotaquira, Miguel

AU - Lang, Roberto M.

AU - Caiani, Enrico G.

N1 - Conference code: 40

PY - 2013

Y1 - 2013

N2 - Quantification of descending aorta atheromas (AT) with 2D transesophageal echocardiography (TEE) is time-consuming and underestimates plaque burden, while 3D TEE can provide the basis for analysis of AT 3D dimensions and volume. We developed a novel semi-automated algorithm to detect aortic AT in 3D TEE, to quantify their severity and volume and tested its accuracy compared to an expert gold standard. The 3D TEE datasets acquired from 58 consecutive patients were analyzed. Results showed a good accuracy in comparison with expert analysis, with an agreement of 95% in absolute presence/absence of AT per patient, 89% in AT number and location and 85% in patient risk due to AT severity classification. AT volumes were highly correlated (R2=0.98), with a slight underestimation (9%) compared to manual measurements. The proposed semi-automated algorithm is rapid, feasible and accurate in analyzing AT from 3D TEE descending aorta datasets.

AB - Quantification of descending aorta atheromas (AT) with 2D transesophageal echocardiography (TEE) is time-consuming and underestimates plaque burden, while 3D TEE can provide the basis for analysis of AT 3D dimensions and volume. We developed a novel semi-automated algorithm to detect aortic AT in 3D TEE, to quantify their severity and volume and tested its accuracy compared to an expert gold standard. The 3D TEE datasets acquired from 58 consecutive patients were analyzed. Results showed a good accuracy in comparison with expert analysis, with an agreement of 95% in absolute presence/absence of AT per patient, 89% in AT number and location and 85% in patient risk due to AT severity classification. AT volumes were highly correlated (R2=0.98), with a slight underestimation (9%) compared to manual measurements. The proposed semi-automated algorithm is rapid, feasible and accurate in analyzing AT from 3D TEE descending aorta datasets.

KW - Three-dimensional displays

KW - Abstracts

KW - Computers

UR - https://www.scopus.com/pages/publications/84894180871

M3 - Conference contribution

AN - SCOPUS:84894180871

SN - 9781479908844

VL - 40

T3 - Computing in Cardiology

SP - 13

EP - 16

BT - Computing in Cardiology 2013, CinC 2013

A2 - Murray, Alan

PB - IEEE Computer Society

T2 - 40th Computing in Cardiology Conference

Y2 - 22 September 2013 through 25 September 2013

ER -

Semi-automated Detection and Quantification of Aortic Atheromas from Three-Dimensional Transesophageal Echocardiography

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