Automated biometry for assessing cephalopelvic disproportion in 3D 0.55T fetal MRI at term

Abstract Fetal MRI offers detailed three-dimensional visualisation of both fetal and maternal pelvic anatomy, allowing for assessment of the risk of cephalopelvic disproportion and obstructed labour. However, conventional measurements of fetal and pelvic proportions and their relative positioning are typically performed manually in 2D, making them time-consuming, subject to inter-observer variability, and rarely integrated into routine clinical workflows. In this work, we present the first fully automated pipeline for pelvic and fetal head biometry in T2-weighted fetal MRI at late gestation. The method employs deep learning-based localisation of anatomical landmarks in 3D reconstructed MRI images, followed by computation of 12 standard linear and circumference measurements commonly used in the assessment of cephalopelvic disproportion. Landmark detection is based on 3D UNet models within MONAI framework, trained on 57 semi-manually annotated datasets. The full pipeline is quantitatively validated on 10 test cases. Furthermore, we demonstrate its clinical feasibility and relevance by applying it to 206 fetal MRI scans (36–40 weeks’ gestation) from the MiBirth study, which investigates prediction of mode of delivery using low field MRI. Competing Interest Statement The authors have declared no competing interest. Funding Statement This work was supported by the MRC grant [MR/X010007/1], the NIHR Advanced Fellowship awarded to Lisa Story [NIHR30166], the Wellcome Trust, Sir Henry Wellcome Fellowship to Jana Hutter [201374/Z/16/Z], DFG Heisenberg funding [502024488], the UKRI, FLF to Jana Hutter [MR/T018119/1], DFG Heisenberg [502024488] the High Tech Agenda Bavaria to Jana Hutter, the Wellcome/ EPSRC Centre for Medical Engineering at Kings College London [WT 203148/Z/16/Z], the NIHR Clinical Research Facility (CRF) at Guys and St Thomas and by the National Institute for Health Research Biomedical Research Centre based at Guys and St Thomas NHS Foundation Trust and Kings College London. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study employs fetal MRI data with research ethics committee (REC) approval by the Health Research Authority: MiBirth: MRI imaging at term for prediction of the mode of birth study (REC: 23/LO/0685). All experiments were performed in accordance with relevant guidelines and regulations. Informed written consent was obtained from all participants. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The individual fetal MRI datasets used in this work are not publicly available due to ethics regulations.