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BOOSTING SKULL-STRIPPING PERFORMANCE FOR PEDIATRIC BRAIN IMAGES.

William Kelley1,2, Nathan Ngo1,2, Adrian V Dalca1,2,3,4

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|October 7, 2024
PubMed
Summary

We developed developmental SynthStrip (d-SynthStrip), a new pediatric skull-stripping tool. It accurately removes non-brain tissue from developing brains, outperforming existing methods for diverse pediatric neuroimaging datasets.

Keywords:
brain extractioninfantmachine learningnewbornpediatric MRIskull-strippingtoddler

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Pediatric Neuroscience

Background:

  • Skull-stripping is crucial for analyzing brain images.
  • Existing tools are often inadequate for pediatric neuroimaging due to developmental variations and image artifacts.
  • There is a growing need for specialized pediatric skull-stripping methods to support large-scale developmental studies.

Purpose of the Study:

  • To introduce developmental SynthStrip (d-SynthStrip), a novel skull-stripping tool specifically designed for pediatric brain images.
  • To address the limitations of adult-centric tools in processing diverse pediatric neuroimaging data.
  • To provide a robust and efficient solution for pediatric neuroimaging data processing.

Main Methods:

  • Developed d-SynthStrip, a skull-stripping model based on an existing robust framework.
  • Utilized a synthesis approach, exposing neural networks to highly variable images generated from label maps.
  • Trained and validated the model on diverse pediatric brain image datasets, considering various scan types and age groups.

Main Results:

  • d-SynthStrip demonstrated superior performance compared to existing pediatric skull-stripping baselines.
  • The model achieved high accuracy across different scan types and pediatric age cohorts.
  • d-SynthStrip offers a fast runtime, completing processing in under one minute, which is competitive with the fastest existing methods.

Conclusions:

  • d-SynthStrip is a highly effective and efficient tool for pediatric brain image skull-stripping.
  • The model's tailored approach addresses the unique challenges of pediatric neuroimaging.
  • This tool will facilitate large-scale multi-institutional pediatric research by enabling reliable data processing.