A Coarse-to-Fine DoubleUNet Framework with Synergistic Loss for Accurate Fetal Head Circumference Measurement

Fetal head circumference (HC) measurement is a routine and indispensable examination during pregnancy, closely associated with fetal health and maternal delivery planning. To reduce the manual workload of sonographers, deep learning has been increasingly applied to this task, with promising outcomes. In this study, we propose a coarse-to-fine deep learning framework for the automatic segmentation and measurement of fetal HC from ultrasound images.

We propose SDUNet, a DoubleUNet-based framework enhanced with a tailored synergistic loss for fetal HC segmentation. The synergistic loss integrates multiple complementary loss terms and dynamically balances their contributions during training. This adaptive mechanism encourages physiologically plausible head shapes while reducing errors caused by speckle noise and boundary ambiguity in ultrasound images. SDUNet was trained on the publicly available HC18 data set and further validated on an independent external data set, without applying any post-processing.

The proposed method achieved superior segmentation and measurement performance. Specifically, it obtained a Dice coefficient of 98.60%, Jaccard index of 97.24%, Hausdorff distance of 0.70 mm, average symmetric surface distance of 0.17 mm, boundary F₁-score of 99.45%, mean absolute error of 1.12 mm and percentage mean absolute error of 0.75%. The method further demonstrated strong generalization on the external data set.

Experimental results demonstrate that the SDUNet framework can effectively segment fetal head regions and provide highly accurate HC measurements.