High Resolution Hippocampus Subfield Segmentation Using Multispectral Multiatlas Patch-Based Label Fusion

  • José E. Romero
  • Pierrick CoupeEmail author
  • José V. Manjón
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9993)


The hippocampus is a brain structure that is involved in several cognitive functions such as memory and learning. It is a structure of great interest due to its relationship to neurodegenerative processes such as the Alzheimer’s disease. In this work, we propose a novel multispectral multiatlas patch-based method to automatically segment hippocampus subfields using high resolution T1-weighted and T2-weighted magnetic resonance images (MRI). The proposed method works well also on standard resolution images after superresolution and consistently performs better than monospectral version. Finally, the proposed method was compared with similar state-of-the-art methods showing better results in terms of both accuracy and efficiency.


Patch Size High Resolution Image Cornu Ammonis Hippocampus Subfield Dimension Voxels 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by the Spanish grant TIN2013-43457-R from the Ministerio de Economia y competitividad. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project “Défi imag’In”.


  1. 1.
    Milner, B.: Psychological defects produced by temporal lobe excision. Res. Publ. Assoc. Res. Nerv. Ment. Dis. 36, 244–257 (1958)Google Scholar
  2. 2.
    Petersen, R., et al.: Memory and MRI-based hippocampal volumes in aging and AD. Neurology 54(3), 581–587 (2000)CrossRefGoogle Scholar
  3. 3.
    Cendes, F., et al.: MRI volumetric measurement of amygdala and hippocampus in temporal lobe epilepsy. Neurology 43(4), 719–725 (1993)CrossRefGoogle Scholar
  4. 4.
    Altshuler, L.L., et al.: Amygdala enlargement in bipolar disorder and hippocampal reduction in schizophrenia: an MRI study demonstrating neuroanatomic specificity. Arch. Gen. Psychiatry 55(7), 663 (1998)Google Scholar
  5. 5.
    Braak, H., Braak, E.: Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82(4), 239–259 (1991)CrossRefGoogle Scholar
  6. 6.
    Chupin, M., et al.: Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19(6), 579–587 (2009)CrossRefGoogle Scholar
  7. 7.
    Jack, C., et al.: Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 52(7), 1397–1403 (1999)CrossRefGoogle Scholar
  8. 8.
    Chakravarty, M., et al.: Performing label-fusion-based segmentation using multiple automatically generated templates. Hum. Brain Mapp. 10(34), 2635–2654 (2013)CrossRefGoogle Scholar
  9. 9.
    Yushkevich, P.A., et al.: Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment. Hum. Brain Mapp. 36(1), 258–287 (2015)CrossRefGoogle Scholar
  10. 10.
    Van Leemput, K., et al.: Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus 19(6), 549–557 (2009)CrossRefGoogle Scholar
  11. 11.
    Iglesias, J.E., et al.: A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. NeuroImage 115(15), 117–137 (2015)CrossRefGoogle Scholar
  12. 12.
    Winterburn, J.L., et al.: A novel in vivo atlas of human hippocampal subfields using high-resolution 3 T magnetic resonance imaging. NeuroImage 74, 254–265 (2013)CrossRefGoogle Scholar
  13. 13.
    Giraud, R., et al.: An optimized patchmatch for multi-scale and multi-feature label fusion. NeuroImage 124, 770–782 (2016)CrossRefGoogle Scholar
  14. 14.
    Pipitone, J.L., et al.: Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage 101(1), 494–512 (2014)CrossRefGoogle Scholar
  15. 15.
    Avants, B.B., et al.: Advanced normalization tools (ANTS). Insight J. 2, 1–35 (2009)Google Scholar
  16. 16.
    Barnes, C., et al.: PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28(3) (2009)Google Scholar
  17. 17.
    Coupé, P., et al.: Adaptive multiresolution non-local means filter for 3D MR image denoising. IET Image Process. 6(5), 558–568 (2012)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Zijdenbos, A.P., et al.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans. Med. Imaging 13(4), 716–724 (1994)CrossRefGoogle Scholar
  19. 19.
    Coupé, P., et al.: Collaborative patch-based super-resolution for diffusion-weighted images. NeuroImage 83, 245–261 (2013)CrossRefGoogle Scholar
  20. 20.
    Manjón, J.V., et al.: Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 31, 192–203 (2010)CrossRefGoogle Scholar
  21. 21.
    Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • José E. Romero
    • 1
  • Pierrick Coupe
    • 2
    • 3
    Email author
  • José V. Manjón
    • 1
  1. 1.Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA)Universitat Politècnica de ValènciaValenciaSpain
  2. 2.University of Bordeaux, LaBRI, UMR 5800, PICTURATalenceFrance
  3. 3.CNRS, LaBRI, UMR 5800, PICTURATalenceFrance

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