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This study introduces a novel deep learning method for automatic infant brain cortical surface parcellation. The new approach avoids surface registration and hand-crafted features, improving accuracy and efficiency in anatomical region analysis.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Automatic parcellation of infant cortical surfaces into anatomical regions of interest (ROIs) is crucial for brain analysis.
  • Conventional methods require surface registration and hand-crafted features, limiting their efficiency and applicability.

Purpose of the Study:

  • To develop a novel, registration-free, and feature-free deep convolutional neural network (DCNN) based method for infant cortical surface parcellation.
  • To improve the accuracy and efficiency of anatomical ROI identification in neonatal brains.

Main Methods:

  • Formulated surface parcellation as a patch-wise classification problem using DCNN.
  • Utilized multi-channel cortical shape descriptors (mean curvature, sulcal depth, average convexity) as DCNN inputs.
  • Employed geodesic-distance-preserving mapping for surface patch projection and graph cuts for spatial consistency enhancement.

Main Results:

  • The proposed DCNN method achieved superior accuracy and efficiency compared to conventional approaches.
  • Validation on 90 neonatal cortical surfaces with manual parcellations demonstrated the method's effectiveness.

Conclusions:

  • The novel DCNN-based approach offers a robust and efficient solution for automatic infant cortical surface parcellation.
  • This method eliminates the need for surface registration and hand-crafted features, advancing brain structural and functional analysis.