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

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


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.


Pancreas Magnetic resonance imaging Volume Fat infiltration 



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.


  1. 1.
    Asaturyan, H., Thomas, E.L., Fitzpatrick, J., Bell, J.D., Villarini, B.: Advancing pancreas segmentation in multi-protocol mri volumes using hausdorff-sine loss function. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 27–35. Springer, Cham (2019). Scholar
  2. 2.
    Ashburner, J., Friston, K.J.: Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. NeuroImage 55(3), 954–967 (2011). Scholar
  3. 3.
    Ashburner, J., Klöppel, S.: Multivariate models of inter-subject anatomical variability. NeuroImage 56(2), 422–439 (2011). Scholar
  4. 4.
    Cai, J., Lu, L., Xing, F., Yang, L.: Pancreas segmentation in CT and MRI via task-specific network design and recurrent neural contextual learning. In: Lu, L., Wang, X., Carneiro, G., Yang, L. (eds.) Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. ACVPR, pp. 3–21. Springer, Cham (2019). Scholar
  5. 5.
    Dholakia, S., Sharples, E.J., Ploeg, R.J., Friend, P.J.: Significance of steatosis in pancreatic transplantation. Transplant. Rev. 31(4), 225–231 (2017). Scholar
  6. 6.
    Gaser, C., Nenadic, I., Buchsbaum, B.R., Hazlett, E.A., Buchsbaum, M.S.: Deformation-based morphometry and its relation to conventional volumetry of brain lateral ventricles in MRI. NeuroImage (2001). Scholar
  7. 7.
    Irving, B., et al.: Deep quantitative liver segmentation and vessel exclusion to assist in liver assessment. Commun. Comput. Inf. Sci. 723, 663–673 (2017). Scholar
  8. 8.
    Isensee, F., Jäger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: Automated design of deep learning methods for biomedical image segmentation 1, 1–8 (2019).
  9. 9.
    Kim, J., et al.: Structural consequences of diffuse traumatic brain injury: alarge deformation tensor-based morphometry study. NeuroImage (2008). Scholar
  10. 10.
    Macauley, M., Percival, K., Thelwall, P.E., Hollingsworth, K.G., Taylor, R.: Altered volume, morphology and composition of the pancreas in type 2 diabetes. PLoS ONE 10(5), 1–14 (2015). Scholar
  11. 11.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016, pp. 565–571 (2016).
  12. 12.
    Mojtahed, A., et al.: Reference range of liver corrected T1 values in a population at low risk for fatty liver disease—a UK Biobank sub-study, with an appendix of interesting cases. Abdom. Radiol. 44(1), 72–84 (2018). Scholar
  13. 13.
    Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas (Midl), 1–10 (2018).
  14. 14.
    Owler, J., Irving, B., Ridgeway, G., Wojciechowska, M., McGonigle, J., Brady, S.M.: Comparison of multi-atlas segmentation and U-Net approaches for automated 3D liver delineation in MRI. In: Zheng, Y., Williams, B.M., Chen, K. (eds.) MIUA 2019. CCIS, vol. 1065, pp. 478–488. Springer, Cham (2020). Scholar
  15. 15.
    Reeder, S.B., Hu, H.H., Sirlin, C.B.: Proton density fat-fraction: a standardized mr-based biomarker of tissue fat concentration. J. Magn. Reson. Imag. JMRI 36(5), 1011 (2012). Scholar
  16. 16.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).
  17. 17.
    Saisho, Y., et al.: Pancreas volumes in humans from birth to age one hundred taking into account sex, obesity, and presence of type-2 diabetes. Clin. Anat. 20(8), 933–942 (2007). Scholar
  18. 18.
    Saisho, Y.: Pancreas volume and fat deposition in diabetes and normal physiology: consideration of the interplay between endocrine and exocrine pancreas. Rev. Diabet. Stud. 13(2–3), 132–147 (2016). Scholar
  19. 19.
    Sudlow, C., et al.: UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12(3), e1001779 (2015). Scholar
  20. 20.
    Tariq, H., Nayudu, S., Akella, S., Glandt, M., Chilimuri, S.: Non-alcoholic fatty pancreatic disease: a review of literature. Gastroenterol. Res. 9(6), 87–91 (2016). Scholar
  21. 21.
    Tarroni, G., et al.: Large-scale quality control of cardiac imaging in population studies: application to UK biobank. Sci. Rep. 10(1), 1–11 (2020). Scholar
  22. 22.
    Villarini, B., Asaturyan, H., Thomas, E.L., Mould, R., Bell, J.D.: A framework for morphological feature extraction of organs from MR images for detection and classification of abnormalities. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), pp. 666–671. IEEE (2017).,
  23. 23.
    Yushkevich, P.A., et al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage 31(3), 1116–1128 (2006). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

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

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