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3D cardiac shape prediction with deep neural networks: simultaneous use of images and patient metadata

3D cardiac shape prediction with deep neural networks: simultaneous use of images and patient metadata
3D cardiac shape prediction with deep neural networks: simultaneous use of images and patient metadata

Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approach, fully automatic large-scale 3D analysis is essential. In this work, we propose a novel deep neural network using both CMR images and patient metadata to directly predict cardiac shape parameters. The proposed method uses the promising ability of statistical shape models to simplify shape complexity and variability together with the advantages of convolutional neural networks for the extraction of solid visual features. To the best of our knowledge, this is the first work that uses such an approach for 3D cardiac shape prediction. We validated our proposed CMR analytics method against a reference cohort containing 500 3D shapes of the cardiac ventricles. Our results show broadly significant agreement with the reference shapes in terms of the estimated volume of the cardiac ventricles, myocardial mass, 3D Dice, and mean and Hausdorff distance.

0302-9743
586-594
Springer Cham
Attar, Rahman
f5efd538-042a-4647-9d46-1370d3049b72
Pereañez, Marco
c050686a-fe7d-4eb7-8ee7-54b2e993d590
Bowles, Christopher
83f97beb-61b1-4760-9f55-f48ab111c8fc
Piechnik, Stefan K.
7de3d548-ca5a-40cb-a52b-c53d2dd2278a
Neubauer, Stefan
c8a34156-a4ed-4dfe-97cb-4f47627d927d
Petersen, Steffen E.
04f2ce88-790d-48dc-baac-cbe0946dd928
Frangi, Alejandro F.
35127be7-3586-4fff-a4a2-bb79cc228141
Shen, Dinggang
Yap, Pew-Thian
Liu, Tianming
Peters, Terry M.
Khan, Ali
Staib, Lawrence H.
Essert, Caroline
Zhou, Sean
Attar, Rahman
f5efd538-042a-4647-9d46-1370d3049b72
Pereañez, Marco
c050686a-fe7d-4eb7-8ee7-54b2e993d590
Bowles, Christopher
83f97beb-61b1-4760-9f55-f48ab111c8fc
Piechnik, Stefan K.
7de3d548-ca5a-40cb-a52b-c53d2dd2278a
Neubauer, Stefan
c8a34156-a4ed-4dfe-97cb-4f47627d927d
Petersen, Steffen E.
04f2ce88-790d-48dc-baac-cbe0946dd928
Frangi, Alejandro F.
35127be7-3586-4fff-a4a2-bb79cc228141
Shen, Dinggang
Yap, Pew-Thian
Liu, Tianming
Peters, Terry M.
Khan, Ali
Staib, Lawrence H.
Essert, Caroline
Zhou, Sean

Attar, Rahman, Pereañez, Marco, Bowles, Christopher, Piechnik, Stefan K., Neubauer, Stefan, Petersen, Steffen E. and Frangi, Alejandro F. (2019) 3D cardiac shape prediction with deep neural networks: simultaneous use of images and patient metadata. Shen, Dinggang, Yap, Pew-Thian, Liu, Tianming, Peters, Terry M., Khan, Ali, Staib, Lawrence H., Essert, Caroline and Zhou, Sean (eds.) In Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. vol. 11765 LNCS, Springer Cham. pp. 586-594 . (doi:10.1007/978-3-030-32245-8_65).

Record type: Conference or Workshop Item (Paper)

Abstract

Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approach, fully automatic large-scale 3D analysis is essential. In this work, we propose a novel deep neural network using both CMR images and patient metadata to directly predict cardiac shape parameters. The proposed method uses the promising ability of statistical shape models to simplify shape complexity and variability together with the advantages of convolutional neural networks for the extraction of solid visual features. To the best of our knowledge, this is the first work that uses such an approach for 3D cardiac shape prediction. We validated our proposed CMR analytics method against a reference cohort containing 500 3D shapes of the cardiac ventricles. Our results show broadly significant agreement with the reference shapes in terms of the estimated volume of the cardiac ventricles, myocardial mass, 3D Dice, and mean and Hausdorff distance.

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More information

Published date: 10 October 2019
Additional Information: Funding Information: Acknowledgements. RA was funded by a PhD scholarship from the School of Computing, University of Leeds. AFF is supported by the Royal Academy of Engineering Chair in Emerging Technologies Scheme (CiET1819\19) and the MedIAN Network (EP/N026993/1) funded by the Engineering and Physical Sciences Research Council (EPSRC). Publisher Copyright: © 2019, Springer Nature Switzerland AG.
Venue - Dates: 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, , Shenzhen, China, 2019-10-13 - 2019-10-17

Identifiers

Local EPrints ID: 480715
URI: http://eprints.soton.ac.uk/id/eprint/480715
ISSN: 0302-9743
PURE UUID: 37c34d1d-3f2c-4a05-85a2-de582170f1e5

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Date deposited: 08 Aug 2023 16:55
Last modified: 05 Jun 2024 19:23

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Contributors

Author: Rahman Attar
Author: Marco Pereañez
Author: Christopher Bowles
Author: Stefan K. Piechnik
Author: Stefan Neubauer
Author: Steffen E. Petersen
Author: Alejandro F. Frangi
Editor: Dinggang Shen
Editor: Pew-Thian Yap
Editor: Tianming Liu
Editor: Terry M. Peters
Editor: Ali Khan
Editor: Lawrence H. Staib
Editor: Caroline Essert
Editor: Sean Zhou

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