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Abstract #2704

Validation of semi-automated analysis of pancreas MRI-PDFF using a scan re-scan cohort from the Long COVID-19 study (COVERSCAN)

Alexandre Triay Bagur1, Michael Brady2, Arun Jandor2, Paul Aljabar2, and Daniel Bulte1
1Department of Engineering Science, University of Oxford, Oxford, United Kingdom, 2Perspectum Ltd, Oxford, United Kingdom


Impairment of the pancreas has been shown in initial findings of the Long COVID-19 study (COVERSCAN), that uses quantitative MRI and expert manual analysis of the pancreas. Quantitative MRI analysis in such large-scale studies benefits from the decision support made possible by advanced image analysis methods. In this work, we validate a semi-automated pancreas processing pipeline that involves manual slice selection and automated segmentation and MRI-PDFF quantification. We show good agreement of the semi-automatic method with the reference manual processing by experts, as well as repeatable quantification in a scan re-scan subset of healthy subjects within COVERSCAN (n=35).

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