Parameter Space CNN for Cortical Surface Segmentation

Conference paper
Part of the Informatik aktuell book series (INFORMAT)


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.


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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

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