Attar, R, Pereañez, M, Gooya, A et al. (6 more authors) (2019) High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort. In: Lecture Notes in Computer Science. MICCAI Medical Image Computing and Computer Assisted Interventions, 16-20 Sep 2018, Granada, Spain. Springer Verlag , pp. 114-121. ISBN 9783030120283
Abstract
The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the cardiac structures, assessment of the segmentation quality, and parsing of cardiac functional indexes. We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset. The proposed pipeline is capable of performing end-to-end image processing including: data organisation, image quality assessment, shape model initialisation, segmentation, segmentation quality assessment, and functional parameter computation; all without any user interaction. To the best of our knowledge, this is the first paper tackling the fully automatic 3D analysis of the UKB population study, providing reference ranges for all key cardiovascular functional indexes, from both left and right ventricles of the heart. We tested our workflow on a reference cohort of 800 healthy subjects for which manual delineations, and reference functional indexes exist. Our results show statistically significant agreement between the manually obtained reference indexes, and those automatically computed using our framework.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2019. This is an author produced version of a paper published in Lecture Notes on Computer Science. Uploaded in accordance with the publisher's self-archiving policy. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-12029-0_13 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Apr 2019 12:03 |
Last Modified: | 25 Apr 2019 12:03 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-3-030-12029-0_13 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145281 |