Estimation of Cardiac Valve Annuli Motion with Deep Learning

Eric Kerfoot; Carlos Escudero King; Tefvik Ismail; David Nordsletten; Renee Miller

doi:10.1007/978-3-030-68107-4_15

Estimation of Cardiac Valve Annuli Motion with Deep Learning

Eric Kerfoot, Carlos Escudero King, Tefvik Ismail, David Nordsletten, Renee Miller^*

^*Corresponding author for this work

Biomedical Engineering Department

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

4 Citations (Scopus)

Abstract

Valve annuli motion and morphology, measured from non-invasive imaging, can be used to gain a better understanding of healthy and pathological heart function. Measurements such as long-axis strain as well as peak strain rates provide markers of systolic function. Likewise, early and late-diastolic filling velocities are used as indicators of diastolic function. Quantifying global strains, however, requires a fast and precise method of tracking long-axis motion throughout the cardiac cycle. Valve landmarks such as the insertion of leaflets into the myocardial wall provide features that can be tracked to measure global long-axis motion. Feature tracking methods require initialisation, which can be time-consuming in studies with large cohorts. Therefore, this study developed and trained a neural network to identify ten features from unlabeled long-axis MR images: six mitral valve points from three long-axis views, two aortic valve points and two tricuspid valve points. This study used manual annotations of valve landmarks in standard 2-, 3- and 4-chamber long-axis images collected in clinical scans to train the network. The accuracy in the identification of these ten features, in pixel distance, was compared with the accuracy of two commonly used feature tracking methods as well as the inter-observer variability of manual annotations. Clinical measures, such as valve landmark strain and motion between end-diastole and end-systole, are also presented to illustrate the utility and robustness of the method.

Original language	English
Title of host publication	Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers
Editors	Esther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	146-155
Number of pages	10
ISBN (Print)	9783030681067
DOIs	https://doi.org/10.1007/978-3-030-68107-4_15
Publication status	Published - 2021
Event	11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020 - Lima, Peru Duration: 4 Oct 2020 → 4 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12592 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020
Country/Territory	Peru
City	Lima
Period	4/10/2020 → 4/10/2020

Keywords

Cardiac valve motion
Deep learning
Landmark detection
Regression network

Access to Document

10.1007/978-3-030-68107-4_15

Cite this

Kerfoot, E., King, C. E., Ismail, T., Nordsletten, D., & Miller, R. (2021). Estimation of Cardiac Valve Annuli Motion with Deep Learning. In E. Puyol Anton, M. Pop, M. Sermesant, V. Campello, A. Lalande, K. Lekadir, A. Suinesiaputra, O. Camara, & A. Young (Eds.), Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers (pp. 146-155). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12592 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-68107-4_15

Kerfoot, Eric ; King, Carlos Escudero ; Ismail, Tefvik et al. / Estimation of Cardiac Valve Annuli Motion with Deep Learning. Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. editor / Esther Puyol Anton ; Mihaela Pop ; Maxime Sermesant ; Victor Campello ; Alain Lalande ; Karim Lekadir ; Avan Suinesiaputra ; Oscar Camara ; Alistair Young. Springer Science and Business Media Deutschland GmbH, 2021. pp. 146-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inbook{99371eb2fcdf46f09f94f11ee8df83d7,

title = "Estimation of Cardiac Valve Annuli Motion with Deep Learning",

abstract = "Valve annuli motion and morphology, measured from non-invasive imaging, can be used to gain a better understanding of healthy and pathological heart function. Measurements such as long-axis strain as well as peak strain rates provide markers of systolic function. Likewise, early and late-diastolic filling velocities are used as indicators of diastolic function. Quantifying global strains, however, requires a fast and precise method of tracking long-axis motion throughout the cardiac cycle. Valve landmarks such as the insertion of leaflets into the myocardial wall provide features that can be tracked to measure global long-axis motion. Feature tracking methods require initialisation, which can be time-consuming in studies with large cohorts. Therefore, this study developed and trained a neural network to identify ten features from unlabeled long-axis MR images: six mitral valve points from three long-axis views, two aortic valve points and two tricuspid valve points. This study used manual annotations of valve landmarks in standard 2-, 3- and 4-chamber long-axis images collected in clinical scans to train the network. The accuracy in the identification of these ten features, in pixel distance, was compared with the accuracy of two commonly used feature tracking methods as well as the inter-observer variability of manual annotations. Clinical measures, such as valve landmark strain and motion between end-diastole and end-systole, are also presented to illustrate the utility and robustness of the method.",

keywords = "Cardiac valve motion, Deep learning, Landmark detection, Regression network",

author = "Eric Kerfoot and King, {Carlos Escudero} and Tefvik Ismail and David Nordsletten and Renee Miller",

note = "Funding Information: This research was supported by the Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King?s College London (WT 203148/Z/16/Z). Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020 ; Conference date: 04-10-2020 Through 04-10-2020",

year = "2021",

doi = "10.1007/978-3-030-68107-4_15",

language = "English",

isbn = "9783030681067",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "146--155",

editor = "{Puyol Anton}, Esther and Mihaela Pop and Maxime Sermesant and Victor Campello and Alain Lalande and Karim Lekadir and Avan Suinesiaputra and Oscar Camara and Alistair Young",

booktitle = "Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers",

address = "Germany",

}

Kerfoot, E, King, CE, Ismail, T, Nordsletten, D & Miller, R 2021, Estimation of Cardiac Valve Annuli Motion with Deep Learning. in E Puyol Anton, M Pop, M Sermesant, V Campello, A Lalande, K Lekadir, A Suinesiaputra, O Camara & A Young (eds), Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12592 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 146-155, 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020, Lima, Peru, 4/10/2020. https://doi.org/10.1007/978-3-030-68107-4_15

Estimation of Cardiac Valve Annuli Motion with Deep Learning. / Kerfoot, Eric; King, Carlos Escudero; Ismail, Tefvik et al.
Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. ed. / Esther Puyol Anton; Mihaela Pop; Maxime Sermesant; Victor Campello; Alain Lalande; Karim Lekadir; Avan Suinesiaputra; Oscar Camara; Alistair Young. Springer Science and Business Media Deutschland GmbH, 2021. p. 146-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12592 LNCS).

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

TY - CHAP

T1 - Estimation of Cardiac Valve Annuli Motion with Deep Learning

AU - Kerfoot, Eric

AU - King, Carlos Escudero

AU - Ismail, Tefvik

AU - Nordsletten, David

AU - Miller, Renee

N1 - Funding Information: This research was supported by the Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King?s College London (WT 203148/Z/16/Z). Publisher Copyright: © 2021, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021

Y1 - 2021

N2 - Valve annuli motion and morphology, measured from non-invasive imaging, can be used to gain a better understanding of healthy and pathological heart function. Measurements such as long-axis strain as well as peak strain rates provide markers of systolic function. Likewise, early and late-diastolic filling velocities are used as indicators of diastolic function. Quantifying global strains, however, requires a fast and precise method of tracking long-axis motion throughout the cardiac cycle. Valve landmarks such as the insertion of leaflets into the myocardial wall provide features that can be tracked to measure global long-axis motion. Feature tracking methods require initialisation, which can be time-consuming in studies with large cohorts. Therefore, this study developed and trained a neural network to identify ten features from unlabeled long-axis MR images: six mitral valve points from three long-axis views, two aortic valve points and two tricuspid valve points. This study used manual annotations of valve landmarks in standard 2-, 3- and 4-chamber long-axis images collected in clinical scans to train the network. The accuracy in the identification of these ten features, in pixel distance, was compared with the accuracy of two commonly used feature tracking methods as well as the inter-observer variability of manual annotations. Clinical measures, such as valve landmark strain and motion between end-diastole and end-systole, are also presented to illustrate the utility and robustness of the method.

AB - Valve annuli motion and morphology, measured from non-invasive imaging, can be used to gain a better understanding of healthy and pathological heart function. Measurements such as long-axis strain as well as peak strain rates provide markers of systolic function. Likewise, early and late-diastolic filling velocities are used as indicators of diastolic function. Quantifying global strains, however, requires a fast and precise method of tracking long-axis motion throughout the cardiac cycle. Valve landmarks such as the insertion of leaflets into the myocardial wall provide features that can be tracked to measure global long-axis motion. Feature tracking methods require initialisation, which can be time-consuming in studies with large cohorts. Therefore, this study developed and trained a neural network to identify ten features from unlabeled long-axis MR images: six mitral valve points from three long-axis views, two aortic valve points and two tricuspid valve points. This study used manual annotations of valve landmarks in standard 2-, 3- and 4-chamber long-axis images collected in clinical scans to train the network. The accuracy in the identification of these ten features, in pixel distance, was compared with the accuracy of two commonly used feature tracking methods as well as the inter-observer variability of manual annotations. Clinical measures, such as valve landmark strain and motion between end-diastole and end-systole, are also presented to illustrate the utility and robustness of the method.

KW - Cardiac valve motion

KW - Deep learning

KW - Landmark detection

KW - Regression network

UR - http://www.scopus.com/inward/record.url?scp=85101494435&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-68107-4_15

DO - 10.1007/978-3-030-68107-4_15

M3 - Conference paper

AN - SCOPUS:85101494435

SN - 9783030681067

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 146

EP - 155

BT - Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers

A2 - Puyol Anton, Esther

A2 - Pop, Mihaela

A2 - Sermesant, Maxime

A2 - Campello, Victor

A2 - Lalande, Alain

A2 - Lekadir, Karim

A2 - Suinesiaputra, Avan

A2 - Camara, Oscar

A2 - Young, Alistair

PB - Springer Science and Business Media Deutschland GmbH

T2 - 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020

Y2 - 4 October 2020 through 4 October 2020

ER -

Kerfoot E, King CE, Ismail T, Nordsletten D, Miller R. Estimation of Cardiac Valve Annuli Motion with Deep Learning. In Puyol Anton E, Pop M, Sermesant M, Campello V, Lalande A, Lekadir K, Suinesiaputra A, Camara O, Young A, editors, Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 146-155. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-68107-4_15

Estimation of Cardiac Valve Annuli Motion with Deep Learning

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this