Dilated Deeply Supervised Networks for Hippocampus Segmentation in MRI

  • Lukas FolleEmail author
  • Sulaiman Vesal
  • Nishant Ravikumar
  • Andreas Maier
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer’s Disease (AD). The shape and structure of the hippocampus are important factors in terms of early AD diagnosis and prognosis by clinicians. However, manual segmentation of such subcortical structures in MR studies is a challenging and subjective task. In this paper, we investigate variants of the well known 3D U-Net, a type of convolution neural network (CNN) for semantic segmentation tasks.We propose an alternative form of the 3D U-Net, which uses dilated convolutions and deep supervision to incorporate multi-scale information into the model. The proposed method is evaluated on the task of hippocampus head and body segmentation in an MRI dataset, provided as part of the MICCAI 2018 segmentation decathlon challenge. The experimental results show that our approach outperforms other conventional methods in terms of different segmentation accuracy metrics.


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  1. 1.
    Ferri C, Prince M, Brayne C, et al. Global prevalence of dementia: a delphi consensus study. Lancet. 2005;366(9503):2112–2117.CrossRefGoogle Scholar
  2. 2.
    Hampel H, Teipel SJ, Buerger K. Neurobiologische Frühdiagnostik der Alzheimer-Krankheit. Nervenarzt. 2007;78:1310–1318.CrossRefGoogle Scholar
  3. 3.
    Schuff N, Woerner N, Boreta L, et al. MRI of hippocampal volume loss in early alzheimer’s disease in relation to ApoE genotype and biomarkers. Brain. 2009;132(4):1067–1077.CrossRefGoogle Scholar
  4. 4.
    Plassard AJ, McHugo M, Heckers S, et al.; International Society for Optics; Photonics. Multi-scale hippocampal parcellation improves atlas-based segmentation accuracy. Proc SPIE. 2017;10133:101332D.Google Scholar
  5. 5.
    Zarpalas D, Gkontra P, Daras P, et al. Accurate and fully automatic hippocampus segmentation using subject-specific 3D optimal local maps into a hybrid active contour model. IEEE J Trans Eng Health Med. 2014;2:1–16.CrossRefGoogle Scholar
  6. 6.
    Ronneberger O, Fischer P, Brox T; Springer. U-net: convolutional networks for biomedical image segmentation. Proc MICCAI. 2015; p. 234–241.Google Scholar
  7. 7.
    Çiçek Ӧ, Abdulkadir A, Lienkamp S, et al. 3D U-net: learning dense volumetric segmentation from sparse annotation. Proc MICCAI. 2016;9901:424–432.Google Scholar
  8. 8.
    Milletari F, Navab N, Ahmadi SA. V-net: fully convolutional neural networks for volumetric medical image segmentation. Proc 3DV. 2016; p. 565–571.Google Scholar
  9. 9.
    Vesal S, Ravikumar N, Maier A. Dilated convolutions in neural networks for left atrial segmentation in 3D gadolinium enhanced-MRI. arXiv:180801673. 2018;.

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Lukas Folle
    • 1
    Email author
  • Sulaiman Vesal
    • 1
  • Nishant Ravikumar
    • 1
  • Andreas Maier
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenDeutschland

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