Automated Fetal Biometry Assessment with Deep Ensembles using Sparse-Sampling of 2D Intrapartum Ultrasound Images

2025-05-20

AI Summary

This study addresses inter- and intra-observer variability in fetal biometry during labor ultrasound and aims to improve the reliability of instrumental vaginal delivery prediction. We propose an end-to-end deep learning framework featuring: (1) a novel sparse frame sampling strategy to mitigate class imbalance and spurious correlations; (2) multi-model ensemble learning to enhance cross-device generalizability; and (3) a hybrid segmentation pipeline combining largest-connected-component filtering with elliptical fitting for high-fidelity fetal skull–symphysis pubis delineation, enabling accurate angle-of-progression (AoP) and head–symphysis distance (HSD) estimation. Evaluated on 224 ultrasound frames from four patients, our method achieves classification accuracy of 0.945, segmentation Dice score of 0.918, and mean absolute errors of 8.90° for AoP and 14.35 mm for HSD—significantly outperforming single-model baselines. The framework delivers a clinically feasible, interpretable, and robust solution for automated labor assessment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The International Society of Ultrasound advocates Intrapartum Ultrasound (US) Imaging in Obstetrics and Gynecology (ISUOG) to monitor labour progression through changes in fetal head position. Two reliable ultrasound-derived parameters that are used to predict outcomes of instrumental vaginal delivery are the angle of progression (AoP) and head-symphysis distance (HSD). In this work, as part of the Intrapartum Ultrasounds Grand Challenge (IUGC) 2024, we propose an automated fetal biometry measurement pipeline to reduce intra- and inter-observer variability and improve measurement reliability. Our pipeline consists of three key tasks: (i) classification of standard planes (SP) from US videos, (ii) segmentation of fetal head and pubic symphysis from the detected SPs, and (iii) computation of the AoP and HSD from the segmented regions. We perform sparse sampling to mitigate class imbalances and reduce spurious correlations in task (i), and utilize ensemble-based deep learning methods for task (i) and (ii) to enhance generalizability under different US acquisition settings. Finally, to promote robustness in task iii) with respect to the structural fidelity of measurements, we retain the largest connected components and apply ellipse fitting to the segmentations. Our solution achieved ACC: 0.9452, F1: 0.9225, AUC: 0.983, MCC: 0.8361, DSC: 0.918, HD: 19.73, ASD: 5.71, $Delta_{AoP}$: 8.90 and $Delta_{HSD}$: 14.35 across an unseen hold-out set of 4 patients and 224 US frames. The results from the proposed automated pipeline can improve the understanding of labour arrest causes and guide the development of clinical risk stratification tools for efficient and effective prenatal care.

Problem

• Automates fetal biometry to reduce measurement variability.
• Classifies standard planes and segments fetal head structures.
• Computes angle of progression and head-symphysis distance reliably.

Innovation

• Sparse-sampling to reduce class imbalances
• Ensemble-based deep learning for generalizability
• Ellipse fitting for structural fidelity