Scanner-based real-time automated volumetry reporting of the fetus, amniotic fluid, placenta and umbilical cord for fetal MRI at 0.55T

TL;DR

This paper presents a real-time, scanner-based automated system for fetal MRI that segments multiple intrauterine regions, estimates fetal weight, and generates reports instantly during scanning, enhancing clinical workflow and accuracy.

Contribution

The study introduces a novel real-time intra-uterine segmentation and reporting pipeline deployed directly on a 0.55T MRI scanner, enabling immediate volumetric analysis and fetal weight estimation.

Findings

High segmentation accuracy (>0.98) for fetus, placenta, and amniotic fluid.

Successful real-time deployment on scanner for 50 cases.

Automated reports generated during MRI acquisition.

Abstract

Purpose: This work aims to enable real-time automated intra-uterine volumetric reporting and fetal weight estimation for fetal MRI, deployed directly on the scanner. Methods: A multi-region segmentation nnUNet was trained on 146 bSSFP images of 73 fetal subjects (coronal and axial orientations) for the parcellation of the fetal head, fetal body, placenta, amniotic fluid and umbilical cord from whole uterus bSSFP stacks. A reporting tool was then developed to integrate the segmentation outputs into an automated report, providing volumetric measurements, fetal weight estimations, and z-score visualisations. The complete pipeline was subsequently deployed on a 0.55T MRI scanner, enabling real-time inference and fully automated reporting in the duration of the acquisition. Results: The segmentation pipeline was quantitatively and retrospectively evaluated on 36 stacks of 18 fetal subjects and demonstrated sufficient performance for all labels, with high scores (>0.98) for the fetus, placenta and amniotic fluid, and 0.91 for the umbilical cord. The prospective evaluation of the scanner deployment step was successfully performed on 50 cases, with the regional volumetric reports available directly on the scanner. Conclusions: This work demonstrated the feasibility of multi-regional intra-uterine segmentation, fetal weight estimation and automated reporting in real-time. This study provides a robust baseline solution for the integration of fully automated scanner-based measurements into fetal MRI reports.