Automated cervix biometry, volumetry and normative models for 3D motion-corrected T2-weighted 0.55-3T fetal MRI during 2nd and 3rd trimesters
Year: 2025
Type: article
Abstract: . Furthermore, there are no clearly formalised protocols or automated methods for MRI cervical measurements. This work introduces the first deep learning pipeline for automated multi-layer segmentation and biometry for 3D T2w reconstructed images of the pregnant cervix. Evaluation on 20 datasets from 0.55T and 3T acquisitions showed good performance in comparison to manual measurements. This solution could potentially minimise the need for manual editing, significantly reduce analysis time and address inter- and intra-observer bias. Next, we used the pipeline to process 270 normal term cases from 16 to 40 weeks gestational age (GA) range. The inlet diameter and length showed the strongest correlation with GA which is in agreement with the gradual remodeling and softening of the cervix prior to birth. We also generated 3D population-averaged MRI atlases of the cervix that are publicly available online.
Source: Scientific Reports
Authors: Alena Uus, Agnieszka Glazewska-Hallin, Simi Bansal, Megan Hall, Charline Bradshaw, Jordina Aviles Verdera, Mary Rutherford, Jana Hutter, Lisa Story
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funders: Wellcome Trust, UK Research and Innovation, NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research, National Institute for Health and Care Research, King's College London, Deutsche Forschungsgemeinschaft, National Institutes of Health, Medical Research Council, Engineering and Physical Sciences Research Council, Menzies Centre for Australian Studies, King's College London, University of London, Centre For Medical Engineering, King’s College London, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G1054121244, G1211483440, G1247657515, King's College London Medical Engineering Centre of Research Excellence, G1419837491, G1814604835, G2084819120, A computer-guided imaging system for prenatal screening and comprehensive diagnosis of fetal abnormalities., G2417159676, G3164596160, Magnetic resonance Imaging of mother and fetus in late gestation to inform and optimise BIRTH management: the MIBIRTH study, Non invasive assessment of human placental function by quantitative MRI, G5296857752, King's College London Medical Engineering Centre of Research Excellence, iFIND technology accelerator, G6027190877, Self-driving MRI, Structure and function of the placenta from implantation to delivery: a next generation MRI approach, G7082644654, Intelligente MR Bildgebung, G8303168876, G8775246936, G8792368603, G938335843, G961285871
OpenAlex ID: W4416837100