Normative volumetric growth modeling of the whole fetal body, placenta, and amniotic fluid for three-dimensional T2-weighted magnetic resonance imaging

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.

Authors

Institutions

Publication Details

Journal
Pediatric Radiology
Published
2026-06-13
DOI
https://doi.org/10.1007/s00247-026-06682-3
Primary Topic
Pregnancy and preeclampsia studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Normative volumetric growth modeling of the whole fetal body, placenta, and amniotic fluid for three-dimensional T2-weighted magnetic resonance imaging

Anangsha Kumar, P. Gil, Maria Deprez, Megan Hall et al.
Pediatric Radiology
Pregnancy and preeclampsia studies
article

Normative volumetric growth modeling of the whole fetal body, placenta, and amniotic fluid for three-dimensional T2-weighted magnetic resonance imaging

Anangsha Kumar, P. Gil, Maria Deprez, Megan Hall, Aysha Luis, Vanessa Kyriakopoulou, Alexia Egloff Collado, Jordina Aviles Verdera, Joseph V. Hajnal, Kathleen Colford, Lisa Story, Sara Neves Silva, Chris Bradshaw, Jana Hutter, Lucilio Cordero‐Grande, Mary Rutherford, Jacqueline Matthew, Hadi Waheed, Alena Uus
article en

Abstract

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.

Pediatric Radiology
Leibniz University Hannover (DE), King's College London (GB), Guy's and St Thomas' NHS Foundation Trust (GB), Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (ES), Universidad Politécnica de Madrid (ES)
Decent work and economic growth
Openalex Percentile: Top 5%
Pregnancy and preeclampsia studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.