Towards automated fetal brain biometry reporting for 3-dimensional T2-weighted 0.55-3T magnetic resonance imaging at 20-40 weeks gestational age range - Luis, Aysha; Uus, Alena; Matthew, Jacqueline; Arulkumaran, Sophie; Egloff Collado, Alexia; Kyriakopoulou, Vanessa; Neves Silva, Sara; Aviles Verdera, Jordina; Hall, Megan; Bansal, Simi; McElroy, Sarah; Colford, Kathleen; et al. (Pediatric Radiology, 2025)
Background: The detailed assessment of fetal brain maturation and development involves morphological evaluation, gyration analysis, and reliable biometric measurements. Manual measurements on conventional 2-D magnetic resonance imaging (MRI) are affected by fetal motion, and there is no clear consensus regarding definitions for brain biometric parameters and anatomical landmark placements, making consistent reference plane and slice selection challenging. Automated biometry with 3-D slice-to-volume reconstruction (SVR) has the potential to improve the reliability of derived measurements, allowing precise quantification of fetal brain development. Previous published works have primarily focused on the technical feasibility of automated fetal brain biometry methods for T2-weighted (T2W) MRI. However, none have proposed solutions for automating the reporting of biometry results, which could enhance clinical utility and support real-time integration into routine clinical workflows. Furthermore, there is no consensus on a universal fetal biometry protocol for 3-D fetal MRI. Objective: To implement and validate a fully automated biometry reporting pipeline for 3-D T2W fetal brain MRI, based on deep learning biometry measurements and computation of z-scores and centiles, by comparison to normative growth charts. Materials and methods: Automated extraction of 13 routinely reported linear fetal biometry measurements using deep learning localization of anatomical landmarks in 3-D reconstructed T2W brain images based on 3-D UNet and presentation of the results in an.html report with centile calculation. The automated biometry method was quantitatively evaluated on 90 retrospective cases against expert manual measurements. Additionally, the fully automated, end-to-end biometry reporting pipeline was prospectively evaluated on 111 cases across a wide range of gestational ages, field strengths, and scanning parameters. We also generated normal centile ranges for 19-40 weeks GA range from 406 normal control datasets. Results: The retrospective quantitative evaluation demonstrated good agreement with manual measurements, with the maximum absolute difference between automated vs. manual measurement within a 1-3-mm range. In the prospective evaluation, more than 98% of landmark placements were graded as acceptable for interpretation and measurements. The processing time of the pipeline was less than 5 min per case, with measurements and centiles available at the time of reporting. Inspection of the automated landmark placement and computed biometrics took 1-3 min per case. The generated normative growth charts demonstrate strong correlation with the trends in the previously reported works. Conclusion: Our approach is the first to develop a fully automated biometry reporting pipeline for 3-D T2-weighted fetal MRI which integrates deep learning-based measurements, centile and z-score calculation vs. normative growth charts and report generation.
CRIS-Team Scopus Importer:2025-11-21
Keywords: Biometry; Deep learning; Fetus; Magnetic resonance imaging; Neuroimaging