Boosting Skull-Stripping Performance for Pediatric Brain Images
William Kelley Nathan Ngo Adrian V. Dalca Bruce Fischl Lilla Zöllei Malte Hoffmann
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 <1-minute runtime
of our tool compares favorably to the fastest baselines. We distribute our
model at https://w3id.org/synthstrip.