@inproceedings{280c6ee9f2d54f7a870e973f0eb8d20e,
title = "3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata",
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.",
author = "Rahman Attar and Marco Perea{\~n}ez and Christopher Bowles and Piechnik, {Stefan K.} and Stefan Neubauer and Petersen, {Steffen E.} and Frangi, {Alejandro F.}",
note = "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: {\textcopyright} 2019, Springer Nature Switzerland AG.; 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",
year = "2019",
doi = "10.1007/978-3-030-32245-8_65",
language = "English",
isbn = "9783030322441",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "586--594",
editor = "Dinggang Shen and Pew-Thian Yap and Tianming Liu and Peters, {Terry M.} and Ali Khan and Staib, {Lawrence H.} and Caroline Essert and Sean Zhou",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings",
address = "United States",
}