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Pancreas Volumetry in UK Biobank: Comparison of Models and Inference at Scale

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12722)

Abstract

The UK Biobank imaging sub-study enables large-scale measurement of pancreas volume, an important biomarker in metabolic disease, including diabetes. Previous methods utilised a pancreas-specific (PS) 3D MRI UK Biobank acquisition to automatically measure pancreas volume. This may lead to a clinically significant underestimation of volume, due to partial coverage of the pancreas in these acquisitions. To address this, we propose a pipeline for the accurate measurement of pancreas volume using stitched whole-body (WB) 3D MRI UK Biobank acquisitions and deep learning-based segmentation. We implement and compare the performance of six different U-Net-like model architectures, leveraging attention layers, recurrent layers, and residual blocks. Furthermore, we investigate pancreas volumetry in 42,313 subjects, separated by sex, and present novel results concerning the change in pancreas volume throughout the course of a day (diurnal variation). To the best of our knowledge, this is the largest pancreas volumetry study to date and the first to propose a pipeline using the whole-body UK Biobank MRI acquisitions to measure pancreas volume.

Keywords

Pancreas segmentation Deep learning UK Biobank 

Notes

Acknowledgements

We would like to acknowledge Perspectum Ltd and the Engineering and Physical Sciences Research Council (EPSRC) for funding and support. This research has been conducted using the UK Biobank resource under application 9914.

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Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Perspectum Ltd.OxfordUK
  2. 2.Department of Engineering ScienceUniversity of OxfordOxfordUK
  3. 3.Department of OncologyUniversity of OxfordOxfordUK

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