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A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
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Cerebellum Parcellation with Convolutional Neural Networks.

Shuo Han1,2, Yufan He3, Aaron Carass3,4

  • 1Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.

Proceedings of Spie--The International Society for Optical Engineering
|May 13, 2020
PubMed
Summary

This study introduces a deep learning method using two 3D CNNs for accurate automatic parcellation of the cerebellum into 28 regions from MR images, outperforming existing methods for neurological disease research.

Keywords:
Cerebellumconvolutional neural networkparcellation

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Artificial Intelligence

Background:

  • Quantitative measurements of cerebellar regions in MR images are crucial for understanding cerebellum-related diseases and functional mapping.
  • Cerebellar atrophy patterns vary across spinocerebellar ataxia (SCA) subtypes and correlate with specific functional losses.
  • Previous automated cerebellum parcellation relied on multi-atlas segmentation, which can be time-consuming and less accurate.

Purpose of the Study:

  • To develop and validate a novel deep learning-based algorithm for automatic cerebellum parcellation into 28 distinct regions.
  • To improve the speed and accuracy of cerebellar sub-region identification in MR images.
  • To provide a tool for enhanced analysis of cerebellar structure in neurological studies.

Main Methods:

  • Utilized two 3D convolutional neural networks (CNNs): a locating network for bounding box prediction and a parcellating network for detailed segmentation.
  • Applied a leave-one-out cross-validation approach on fifteen manually delineated MR images.
  • Developed a Singularity container for public accessibility of the algorithm.

Main Results:

  • The proposed CNN-based algorithm demonstrated superior Dice coefficients compared to a state-of-the-art multi-atlas segmentation method.
  • Achieved accurate parcellation of the cerebellum into 28 regions.
  • Successfully applied the algorithm to MR images from healthy subjects and patients with SCA6 and SCA8.

Conclusions:

  • The developed deep learning algorithm offers a highly accurate and efficient method for automatic cerebellum parcellation from MR images.
  • This tool has the potential to significantly advance the study of cerebellar diseases and functional mapping.
  • The public availability of the algorithm facilitates broader research and clinical applications.