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Pancreas Segmentation-Derived Biomarkers: Volume and Shape Metrics in the UK Biobank Imaging Study

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Medical Image Understanding and Analysis (MIUA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1248))

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Abstract

Quantitative imaging biomarkers derived from magnetic resonance imaging of the pancreas could reveal changes in pancreas organ volume and shape manifest in chronic disease. Recent developments in machine learning facilitate pancreas segmentation and volume extraction. Machine learning methods could also help in designing a data-driven approach to pancreas shape characterization. We present an automated pipeline for pancreas volume and shape characterization. We start off with deep learning-based segmentation; we show the impact of choice of loss function in pancreas segmentation by comparing a 3D U-Net model trained using soft Dice over cross-entropy loss. Then, a diffeomorphic algorithm for group-wise registration as well as manifold learning are used to extract prominent shape features from the segmentation masks. The technique shows potential in a subset (N = 3,909) of the UK Biobank imaging sub-study for (1) automated quality control, e.g. suboptimal pancreas coverage acquisitions; and (2) determining abnormal pancreas morphology, that might reflect different patterns of fat infiltration. To our knowledge, this work is the first to attempt learning pancreas shape features.

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Notes

  1. 1.

    SPM12 revision 7771, https://www.fil.ion.ucl.ac.uk/spm/, under MATLAB R2019b.

  2. 2.

    scikit-learn version 0.22.1, https://scikit-learn.org/stable/.

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Acknowledgements

We would like to thank Dr Benjamin Irving and James Owler for the development of the deep learning segmentation framework and Dr Rachel Phillips for advice with manual pancreas annotations in radiology images.

We would also like to acknowledge EPSRC and Perspectum Ltd. for funding and support.

This research has been conducted using the UK Biobank Resource under application 9914.

Author information

Authors and Affiliations

  1. Department of Engineering Science, University of Oxford, Oxford, UK

    Alexandre Triay Bagur & Daniel Bulte

  2. Perspectum Ltd., Oxford, UK

    Alexandre Triay Bagur, Ged Ridgway, John McGonigle & Sir Michael Brady

  3. Department of Oncology, University of Oxford, Oxford, UK

    Sir Michael Brady

Authors
  1. Alexandre Triay Bagur
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  2. Ged Ridgway
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Corresponding author

Correspondence to Alexandre Triay Bagur .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Bartłomiej W. Papież

  2. University of Oxford, Oxford, UK

    Ana I. L. Namburete

  3. University of Oxford, Oxford, UK

    Mohammad Yaqub

  4. University of Oxford, Oxford, UK

    J. Alison Noble

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Bagur, A.T., Ridgway, G., McGonigle, J., Brady, S.M., Bulte, D. (2020). Pancreas Segmentation-Derived Biomarkers: Volume and Shape Metrics in the UK Biobank Imaging Study. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_11

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  • DOI: https://doi.org/10.1007/978-3-030-52791-4_11

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