Boosting Skull-Stripping Performance for Pediatric Brain Images

Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain dev...

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Bibliographic Details
Published in:2024 IEEE International Symposium on Biomedical Imaging (ISBI) 2024-05, Vol.2024, p.1-5
Main Authors: Kelley, William, Ngo, Nathan, Dalca, Adrian V., Fischl, Bruce, Zollei, Lilla, Hoffmann, Malte
Format: Article
Language:English
Subjects:
Biological system modeling
brain extraction
Brain modeling
Buildings
Data acquisition
infant
machine learning
Magnetic resonance imaging
newborn
pediatric MRI
Runtime
skull-stripping
Sociology
toddler
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Summary:Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI56570.2024.10635307