Skip to main content
Book cover

International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 pp 284–291Cite as

Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11766)

Abstract

Accurate segmentations of neuroanatomical structures are essential for volumetric and morphological assessment, but manual segmentation is time-consuming and error-prone. We propose a convolutional neural network for structural segmentation based on deformation of an example mask that is disease-state agnostic, which we apply to the hippocampus. The hippocampus is one of the first subcortical structures affected by Alzheimer’s disease, atrophying as the disease progresses. As the disease state may be unknown, and due to the varying degrees of atrophy, an accurate shape prior is not always available. The proposed network is based on an adapted spatial transformer network that learns a deformation field to resample an initial binary mask, to create an output segmentation. This segmentation is learnt by the network from the input T1-weighted MRI in an end-to-end manner. Experiments on the HarP dataset show that the network outperforms other segmentation methods and is consistent across disease states, independent of the degree of disease-related atrophy. We also explore the effect of the initial binary mask on the segmentation, showing that the it is insensitive to the size and initial location of the binary mask.

Keywords

  • Segmentation
  • Spatial transformer
  • Hippocampus

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-32248-9_32
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-32248-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

Notes

  1. 1.

    Software download: fsl.fmrib.ox.ac.uk/fsldownloads_registration.

  2. 2.

    Software download: surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall.

References

  1. Mufson, E.J., Mahady, L., Waters, D., et al.: Hippocampal plasticity during the progression of Alzheimer’s Disease. Neuroscience 309, 51–67 (2015)

    CrossRef  Google Scholar 

  2. Flores, R., Joie, R., Chetelat, G.: Structural imaging of the hippocampal subfields in healthy aging and Alzheimer’s disease. Neuroscience 309, 29–50 (2015)

    CrossRef  Google Scholar 

  3. Fischl, B., Salat, D.H., et al.: Whole brain segmentation: automated labelling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002)

    CrossRef  Google Scholar 

  4. Patenaude, B., Smith, S.M., et al.: A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56(3), 907–922 (2011)

    CrossRef  Google Scholar 

  5. Roy, A., Conjeti, S., Navab, N., Wachinger, C.: QuickNAT: segmenting MRI neuroanatomy in 20 seconds. NeuroImage 186, 713–727 (2019)

    CrossRef  Google Scholar 

  6. Wachinger, C., Reuter, M., Klein, T.: DeepNAT: deep convolutional neural network for segmenting neuroanatomy. NeuroImage 170, 434–445 (2017)

    CrossRef  Google Scholar 

  7. Balakrishnan, G., Zhao, A., Sabuncu, M., Guttag, J., Dalca, A.: VoxelMorph: a learning framework for deformable medical image registration. arXiv (2018)

    Google Scholar 

  8. Jaderberg, M., Simonyan, K., Zisserman, A., Kuvukcuoglu, K.: Spatial transformer networks. In: Neural Information Processing Systems (2015)

    Google Scholar 

  9. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49

    CrossRef  Google Scholar 

  10. Boccardi, M., Bocchetta, M., Morency, F.C., et al.: Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol. Alzheimer’s Dementia 11(2), 175–183 (2015)

    CrossRef  Google Scholar 

Download references

Acknowledgements

This work was supported by funding from the Engineering and Physical Sciences Research Council (EPSRC) and Medical Research Council (MRC) [grant number EP/L016052/1]. A. Namburete is grateful for support from the UK Royal Academy of Engineering under the Engineering for Development Research Fellowships scheme. M. Jenkinson is supported by the National Institute for Health Research (NIHR) and the Oxford Biomedical Research Centre (BRC). Computation used the Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by Health Data Research UK and the NIHR Oxford Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

Author information

Authors and Affiliations

  1. Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, Oxford, UK

    Nicola K. Dinsdale & Mark Jenkinson

  2. Insitute of Biomedical Engineering, University of Oxford, Oxford, UK

    Ana I. L. Namburete

Authors
  1. Nicola K. Dinsdale
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Jenkinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ana I. L. Namburete
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicola K. Dinsdale .

Editor information

Editors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Dinggang Shen

  2. University of Georgia, Athens, GA, USA

    Prof. Tianming Liu

  3. Western University, London, ON, Canada

    Dr. Terry M. Peters

  4. Yale University, New Haven, CT, USA

    Lawrence H. Staib

  5. University of Strasbourg, Illkirch, France

    Caroline Essert

  6. United Imaging Intelligence, Shanghai, China

    Sean Zhou

  7. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Prof. Dr. Pew-Thian Yap

  8. Western University, London, ON, Canada

    Ali Khan

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Dinsdale, N.K., Jenkinson, M., Namburete, A.I.L. (2019). Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-32248-9_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32247-2

  • Online ISBN: 978-3-030-32248-9

  • eBook Packages: Computer ScienceComputer Science (R0)