Automated Biometry for Assessing Cephalopelvic Disproportion in 3D 0.55T Fetal MRI at Term
Uus AU, Bansal S, Gerek Y, Waheed H, Silva SN, Verdera JA, Kyriakopoulou V, Betti L, Jaufuraully S, Hajnal JV, Siasakos D, David A, Chandiramani M, Hutter J, Story L, Rutherford MA (2026)
Publication Type: Conference contribution
Publication year: 2026
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 16118 LNCS
Pages Range: 130-140
Conference Proceedings Title: Lecture Notes in Computer Science
Event location: Daejeon
ISBN: 9783032059963
DOI: 10.1007/978-3-032-05997-0_12
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.
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APA:
Uus, A.U., Bansal, S., Gerek, Y., Waheed, H., Silva, S.N., Verdera, J.A.,... Rutherford, M.A. (2026). Automated Biometry for Assessing Cephalopelvic Disproportion in 3D 0.55T Fetal MRI at Term. In Daphna Link-Sourani, Esra Abaci Turk, Wietske Bastiaansen, Jana Hutter, Andrew Melbourne, Roxane Licandro (Eds.), Lecture Notes in Computer Science (pp. 130-140). Daejeon, KR: Springer Science and Business Media Deutschland GmbH.
MLA:
Uus, Alena U., et al. "Automated Biometry for Assessing Cephalopelvic Disproportion in 3D 0.55T Fetal MRI at Term." Proceedings of the 10th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2025, Held in Conjunction with 28th International Conference on Medical Imaging and Computer Assisted Intervention, MICCAI 2025, Daejeon Ed. Daphna Link-Sourani, Esra Abaci Turk, Wietske Bastiaansen, Jana Hutter, Andrew Melbourne, Roxane Licandro, Springer Science and Business Media Deutschland GmbH, 2026. 130-140.
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