Uthoff, J., Alabed, S., Swift, A.J. et al. (1 more author) (2020) Geodesically smoothed tensor features for pulmonary hypertension prognosis using the heart and surrounding tissues. In: Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D. and Joskowicz, L., (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. 23rd International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI 2020), 04-08 Oct 2020, Lima, Peru. Springer Nature Switzerland , pp. 253-262. ISBN 9783030597122
Abstract
Cardiac magnetic resonance imaging (CMRI) provides non-invasive characterization of the heart and surrounding tissues. It is an important tool for the prognosis of pulmonary arterial hypertension (PAH), a disease with heterogeneous presentation that makes survival likelihood prediction a challenging task. In this paper, we propose a Geodesically Smooothed Tensor feature learning method (GST) that utilizes not only the heart but also its surrounding tissues to characterize disease severity for improving prognosis. Specifically, GST includes structures surrounding the heart by geodesic rings which were incrementally smoothed with Gaussian filters. This provides additive insight while modulating for patient positional differences for a subsequent tensor-based feature learning pipeline. We performed evaluation on Four Chamber and Short Axis CMRI from 150 individuals with confirmed PAH and 1-year mortality census (27 deceased, 123 alive). The proposed GST method improved AUC and Cox difference at 4-years post-imaging (Cox4YD) over the standardized measurement of right ventricular end systolic volume index (RVESVi: AUC: 0.58; Cox4YD: 0.18) on the Four Chamber protocol (AUC: 0.77; Cox4YD: 0.35). Only AUC was improved over RVESVi in the Short Axis scans (AUC: 0.77; Cox4YD: 0.16).
Metadata
Item Type: | Proceedings Paper |
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Copyright, Publisher and Additional Information: | © 2020 Springer Nature Switzerland AG. This is an author-produced version of a paper subsequently published in Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12262. Uploaded in accordance with the publisher's self-archiving policy. |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Jul 2020 07:33 |
Last Modified: | 16 Oct 2020 08:48 |
Status: | Published |
Publisher: | Springer Nature Switzerland |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-030-59713-9_25 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163177 |