Abstract Background Magnetic resonance imaging (MRI)-based volumetry of the fetus, placenta, and amniotic fluid is clinically valuable but rarely used due to labor-intensive manual segmentation of motion-corrupted two-dimensional (2-D) stacks. Existing deep learning approaches are typically limited to single structures and 2-D data, while no robust automated solution exists for whole-uterus volumetry in reconstructed three-dimensional (3-D) MRI, and normative reference ranges are lacking. Objective To develop an automated pipeline for whole-uterus volumetry in 3-D T2-weighted fetal MRI and derive normative growth models for fetal, placental, and amniotic fluid volumes. Materials and methods Motion-corrupted T2-weighted stacks (0.55–3-T field strength) were reconstructed into 3-D isotropic images using deformable slice-to-volume reconstruction, followed by automated segmentation with a 3-D U-Net. The method was applied to 357 normal-control datasets with confirmed term birth (16–41 weeks gestational age range) to derive quadratic normative growth curves. Performance and clinical utility were further evaluated on 43 independent datasets. Results Segmentation was highly accurate (Dice: fetus 0.997, placenta 0.995, amniotic fluid 0.998) with low volume errors (<1%) and minimal manual refinement required in <25% of cases. In the control cohort, fetal and placental volumes increased with gestational age ( P <0.001), while amniotic fluid followed a quadratic trend. Longitudinal growth rates were 146.6 cc/week (fetus) and 38.8 cc/week (placenta). Preterm pregnancies showed significantly lower fetal and placental volumes ( P <0.001) and reduced amniotic fluid ( P <0.01). Conclusion This work presents the first automated pipeline for simultaneous whole-uterus volumetry in 3-D fetal MRI and establishes normative growth models across gestation. The approach enables accurate, standardized volumetric assessment and provides a practical tool for detecting abnormal growth patterns in both normal and high-risk pregnancies.
Uus et al. (Sat,) studied this question.