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Bohoran, T.A., Parke, K.S., Graham-Brown, M.P.M., Meisuria, M., Singh, A., Wormleighton, J., Adlam, D., Gopalan, D., Davies, M.J., Williams, B., Brown, M., McCann, G.P. and Giannakidis, A. 
ORCID: 0000-0001-7403-923X,
2023.
Resource efficient aortic distensibility calculation by end to end spatiotemporal learning of aortic lumen from multicentre multivendor multidisease CMR images.
Scientific Reports, 13: 21794.
ISSN 2045-2322
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Text
1843775_Giannakidis.pdf - Published version Download (4MB) | Preview |
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∼3.9 times less fuel and generates∼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.
| Item Type: | Journal article | ||||||
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| Publication Title: | Scientific Reports | ||||||
| Creators: | Bohoran, T.A., Parke, K.S., Graham-Brown, M.P.M., Meisuria, M., Singh, A., Wormleighton, J., Adlam, D., Gopalan, D., Davies, M.J., Williams, B., Brown, M., McCann, G.P. and Giannakidis, A. | ||||||
| Publisher: | Springer | ||||||
| Date: | 8 December 2023 | ||||||
| Volume: | 13 | ||||||
| ISSN: | 2045-2322 | ||||||
| Identifiers: |
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| Rights: | © the author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | ||||||
| Divisions: | Schools > School of Science and Technology | ||||||
| Record created by: | Jonathan Gallacher | ||||||
| Date Added: | 04 Jan 2024 11:18 | ||||||
| Last Modified: | 04 Jan 2024 11:18 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/50620 |
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