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BIBSNet: A deep learning baby image brain segmentation network for MRI scans.

Timothy J Hendrickson1, Paul Reiners2, Lucille A Moore2

  • 1Minnesota Supercomputing Institute, University of Minnesota, USA; Masonic Institute for the Developing Brain, University of Minnesota, USA.

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Summary

A new deep neural network, Baby and Infant Brain Segmentation Neural Network (BIBSNet), significantly improves infant brain MRI segmentation accuracy. This faster, open-source model enhances the study of early brain development.

Keywords:
Anatomical Brain Tissue SegmentationDeep LearningInfancyMRIProcessing Methods

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Area of Science:

  • Neuroimaging
  • Developmental Neuroscience
  • Medical Image Analysis

Background:

  • Infant brain segmentation is crucial for understanding neurodevelopment.
  • The rapidly changing infant brain presents unique segmentation challenges for existing algorithms.

Purpose of the Study:

  • Introduce BIBSNet (Baby and Infant Brain Segmentation Neural Network), a novel deep neural network for infant brain MRI segmentation.
  • Develop a robust and generalizable model for segmenting brain tissues in early postnatal development.

Main Methods:

  • Trained BIBSNet on MR brain images from 90 infants (0-8 months) using manually annotated and synthetic data.
  • Employed a 10-fold cross-validation procedure for model training and assessment.
  • Evaluated performance using Dice Similarity Coefficient (DSC) and compared with Joint Label Fusion (JLF) and iBeat.

Main Results:

  • BIBSNet demonstrated superior segmentation accuracy compared to JLF, particularly for gray matter (0.849 vs. 0.713) and white matter (0.862 vs. 0.791).
  • BIBSNet-derived anatomical and functional metrics outperformed JLF across most measures.
  • No significant difference between BIBSNet and iBeat for infants 0-5 months; iBeat performed better for 6-8 months.

Conclusions:

  • BIBSNet offers a significant improvement over JLF for infant brain segmentation across all analyzed age groups.
  • BIBSNet is 600x faster than JLF, produces compatible labels, and integrates easily into processing pipelines.
  • BIBSNet presents a viable and efficient solution for segmenting infant brains during critical developmental stages.