Automated Pediatric Brain Tumor Imaging Assessment Tool From CBTN: Enhancing Suprasellar Region Inclusion And Managing Limited Data With Deep Learning

Fully automatic skull-stripping and tumor segmentation are crucial for monitoring pediatric brain tumors (PBT). Current methods, however, often lack generalizability, particularly for rare tumors in the sellar/suprasellar regions and when applied to real-world clinical data in limited data scenarios. To address these challenges, we propose AI-driven techniques for skull-stripping and tumor segmentation.