Population-Driven Synthesis of Personalized Cranial Development From Cross-Sectional Pediatric CT Images.
Journal:
IEEE transactions on bio-medical engineering
Published Date:
Sep 1, 2025
Abstract
OBJECTIVE: Predicting normative pediatric growth is crucial to identify developmental anomalies. While traditional statistical and computational methods have shown promising results predicting personalized development, they either rely on statistical assumptions that limit generalizability or require longitudinal datasets, which are scarce in children. Recent deep learning methods trained with cross-sectional dataset have shown potential to predict temporal changes but have only succeeded at predicting local intensity changes and can hardly model major anatomical changes that occur during childhood. We present a novel deep learning method for image synthesis that can be trained using only cross-sectional data to make personalized predictions of pediatric development.
