Automatic Myocardial Disease Prediction from Delayed-Enhancement Cardiac MRI and Clinical Information

TY - CHAP

T1 - Automatic Myocardial Disease Prediction from Delayed-Enhancement Cardiac MRI and Clinical Information

AU - Lourenço, Ana

AU - Kerfoot, Eric

AU - Grigorescu, Irina

AU - Scannell, Cian M.

AU - Varela, Marta

AU - Correia, Teresa M.

N1 - Funding Information: Acknowledgments. This work was supported by the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z] and the British Heart Foundation Centre of Research Excellence at Imperial College London [RE/18/4/34215]. Publisher Copyright: © 2021, Springer Nature Switzerland AG.

PY - 2021

Y1 - 2021

N2 - Delayed-enhancement cardiac magnetic resonance (DE-CMR) provides important diagnostic and prognostic information on myocardial viability. The presence and extent of late gadolinium enhancement (LGE) in DE-CMR is negatively associated with the probability of improvement in left ventricular function after revascularization. Moreover, LGE findings can support the diagnosis of several other cardiomyopathies, but their absence does not rule them out, making disease classification by visual assessment difficult. In this work, we propose deep learning neural networks that can automatically predict myocardial disease from patient clinical information and DE-CMR. All the proposed networks achieved very good classification accuracy (>85%). Including information from DE-CMR (directly as images or as metadata following DE-CMR segmentation) is valuable in this classification task, improving the accuracy to 95–100%.

AB - Delayed-enhancement cardiac magnetic resonance (DE-CMR) provides important diagnostic and prognostic information on myocardial viability. The presence and extent of late gadolinium enhancement (LGE) in DE-CMR is negatively associated with the probability of improvement in left ventricular function after revascularization. Moreover, LGE findings can support the diagnosis of several other cardiomyopathies, but their absence does not rule them out, making disease classification by visual assessment difficult. In this work, we propose deep learning neural networks that can automatically predict myocardial disease from patient clinical information and DE-CMR. All the proposed networks achieved very good classification accuracy (>85%). Including information from DE-CMR (directly as images or as metadata following DE-CMR segmentation) is valuable in this classification task, improving the accuracy to 95–100%.

KW - Cardiac MRI

KW - Classification

KW - Late gadolinium enhancement

KW - Myocardial infarction

UR - https://www.scopus.com/pages/publications/85101509255

U2 - 10.1007/978-3-030-68107-4_34

DO - 10.1007/978-3-030-68107-4_34

M3 - Conference paper

AN - SCOPUS:85101509255

SN - 9783030681067

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

SP - 334

EP - 341

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 -