Robust Fetal Pose Estimation across Gestational Ages via Cross-Population Augmentation

Sebastian Diaz

Abstract:

Fetal motion is a critical indicator of neurological develop-
ment and intrauterine health, yet its quantification remains challenging,
particularly at earlier gestational ages (GA). Current methods track fe-
tal motion by predicting the location of annotated landmarks on 3D
echo planar imaging (EPI) time-series, primarily in third-trimester fe-
tuses. The predicted landmarks enable simplification of the fetal body
for downstream analysis. While these methods perform well within their
training age distribution, they consistently fail to generalize to early GAs
due to significant anatomical changes in both mother and fetus across
gestation, as well as the difficulty of obtaining annotated early GA EPI
data. In this work, we develop a cross-population data augmentation
framework that enables pose estimation models to robustly generalize to
younger GA clinical cohorts using only annotated images from older GA
cohorts. Specifically, we introduce a fetal-specific augmentation strategy
that simulates the distinct intrauterine environment and fetal positioning
of early GAs. Our experiments find that cross-population augmentation
yields reduced variability and significant improvements across both older
GA and challenging early GA cases. By enabling more reliable pose es-
timation across gestation, our work potentially facilitates early clinical
detection and intervention in challenging 4D fetal imaging settings. Code
is available at https://github.com/sebodiaz/cross-population-pose.