Advertisement

Parameter Space CNN for Cortical Surface Segmentation

  • Leonie Henschel
  • Martin ReuterEmail author
Conference paper
  • 30 Downloads
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Spherical coordinate systems have become a standard for analyzing human cortical neuroimaging data. Surface-based signals, such as curvature, folding patterns, functional activations, or estimates of myelination define relevant cortical regions. Surface-based deep learning approaches, however, such as spherical CNNs primarily focus on classification and cannot yet achieve satisfactory accuracy in segmentation tasks. To perform surface-based segmentation of the human cortex, we introduce and evaluate a 2D parameter space approach with view aggregation (p3CNN). We evaluate this network with respect to accuracy and show that it outperforms the spherical CNN by a margin, increasing the average Dice similarity score for cortical segmentation to above 0.9.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. 1.
    Fischl B. FreeSurfer. Neuroimage. 2012;62:774–781.Google Scholar
  2. 2.
    Jiang CM, Huang J, Kashinath K, et al. Spherical CNNs on unstructured grids. In: International Conference on Learning Representations; 2019. .Google Scholar
  3. 3.
    Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks. In: Proc IEEE CVPR. vol. 1; 2017. p. 3.Google Scholar
  4. 4.
    Poldrack RA, Congdon E, Triplett W, et al. A phenome-wide examination of neural and cognitive function. Scientific Data. 2016;3:160110.Google Scholar
  5. 5.
    Mueller SG, Weiner MW, Thal LJ, et al. Ways toward an early diagnosis in alzheimer’s disease: the alzheimer’s disease neuroimaging initiative (ADNI). Alzheimers Dement. 2005;1:55–66.Google Scholar
  6. 6.
    Malone IB, Cash D, Ridgway GR, et al. MIRIAD–Public release of a multiple time point alzheimer’s MR imaging dataset. Neuroimage. 2013;70:33–36.Google Scholar
  7. 7.
    Marcus DS, Wang TH, Parker J, et al. Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J Cogn Neurosci. 2007;19:1498–1507.Google Scholar
  8. 8.
    Di Martino A, O’connor D, Chen B, et al. Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Scientific Data. 2017;4:170010.Google Scholar
  9. 9.
    Van Essen DC, Ugurbil K, Auerbach E, et al. The human connectome project: a data acquisition perspective. Neuroimage. 2012;62:2222–2231.Google Scholar
  10. 10.
    Klein A, Tourville J. 101 labeled brain images and a consistent human cortical labeling protocol. Front Neurosci. 2012;6:171.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  1. 1.German Center for Neurodegenerative Diseases (DZNE)BonnDeutschland
  2. 2.A.A. Martinos Center for Biomedical ImagingMGHBostonUSA
  3. 3.Department of RadiologyHarvard Medical SchoolBostonUSA

Personalised recommendations