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Resource efficient aortic distensibility calculation by end to end spatiotemporal learning of aortic lumen from multicentre multivendor multidisease CMR images.

Published version
Peer-reviewed

Repository DOI


Change log

Authors

Bohoran, Tuan Aqeel 
Parke, Kelly S 
Graham-Brown, Matthew PM 
Meisuria, Mitul 
Singh, Anvesha 

Abstract

Aortic distensibility (AD) is important for the prognosis of multiple cardiovascular diseases. We propose a novel resource-efficient deep learning (DL) model, inspired by the bi-directional ConvLSTM U-Net with densely connected convolutions, to perform end-to-end hierarchical learning of the aorta from cine cardiovascular MRI towards streamlining AD quantification. Unlike current DL aortic segmentation approaches, our pipeline: (i) performs simultaneous spatio-temporal learning of the video input, (ii) combines the feature maps from the encoder and decoder using non-linear functions, and (iii) takes into account the high class imbalance. By using multi-centre multi-vendor data from a highly heterogeneous patient cohort, we demonstrate that the proposed method outperforms the state-of-the-art method in terms of accuracy and at the same time it consumes [Formula: see text] 3.9 times less fuel and generates [Formula: see text] 2.8 less carbon emissions. Our model could provide a valuable tool for exploring genome-wide associations of the AD with the cognitive performance in large-scale biomedical databases. By making energy usage and carbon emissions explicit, the presented work aligns with efforts to keep DL's energy requirements and carbon cost in check. The improved resource efficiency of our pipeline might open up the more systematic DL-powered evaluation of the MRI-derived aortic stiffness.

Description

Keywords

Humans, Aorta, Carbon, Cardiovascular Diseases, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Magnetic Resonance Imaging, Cine, Multicenter Studies as Topic

Journal Title

Sci Rep

Conference Name

Journal ISSN

2045-2322
2045-2322

Volume Title

13

Publisher

Springer Science and Business Media LLC
Sponsorship
HORIZON EUROPE Marie Sklodowska-Curie Actions (801604)