Automated Fetal Biometry Assessment with Deep Ensembles using Sparse-Sampling of 2D Intrapartum Ultrasound Images
Journal:
arXiv
Published Date:
May 20, 2025
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.
