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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.
Arxiv
|March 11, 2024
Summary
Pediatric skull-stripping tools are needed for brain development research. Developmental SynthStrip (d-SynthStrip) offers a robust, fast solution outperforming existing methods for analyzing pediatric brain images.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Pediatric Neuroscience
Background:
- Skull-stripping is crucial for analyzing brain images, but adult-specific tools fail with pediatric data due to developmental variations and image artifacts.
- Existing tools are inadequate for the unique challenges of pediatric neuroimaging, hindering large-scale studies of brain development.
Approach:
- Developed developmental SynthStrip (d-SynthStrip), a novel skull-stripping model specifically for pediatric brain images.
- Utilized a framework exposing neural networks to synthesized, highly variable images derived from label maps to enhance robustness.
Key Points:
- d-SynthStrip significantly outperforms existing pediatric skull-stripping methods across various scan types and age groups.
- The model achieves a runtime of under one minute, making it highly efficient for processing large datasets.
- The tool demonstrates superior accuracy and robustness in handling the complexities of developing brains.
Conclusions:
- d-SynthStrip provides a well-tested, efficient, and accurate solution for pediatric skull-stripping.
- This advancement supports multi-institutional efforts to understand perinatal brain development through improved data processing.
- The d-SynthStrip model is publicly available for research use.
