U‐Net‐Based Fetal Head Circumference Segmentation With Synthetic‐Driven Generation Data Augmentation

Abstract

ABSTRACT In modern healthcare, deep learning methods have gained considerable attention in analyzing medical imaging over the last few years, achieving reliable outcomes in various segmentation tasks. However, these models heavily rely on high‐quality and large annotated samples, which remain a scarce and hard resource to acquire in several medical fields, particularly in obstetrics and gynecology, limiting the deep learning models' capability to generalize effectively on unseen datasets. Therefore, this study proposes a two‐stage framework that enhances the model segmentation performance by incorporating synthetic ultrasound image generation for fetal head segmentation and thus measures its circumference. In this paper, we propose a novel two‐stage pipeline designed to segment the fetal head circumference. Initially, an improved Deep Convolutional Generative Adversarial Network is employed to generate synthetic fetal head ultrasound images, producing high‐quality and high structural similarity to real ones. Preliminary annotations were obtained through automated segmentation using a lightweight U‐Net, followed by a refined enhancement phase. These annotations were incorporated progressively into the U‐Net training to enhance the model's performance and effectiveness. The proposed framework was evaluated on the HC18 Grand Challenge dataset. The GAN‐based synthetic images achieved a Peak Signal‐to‐Noise Ratio of 56 and a Structural Similarity Index Measure of 0.99, demonstrating the diversity and similarity of the images and thus significantly improving the U‐Net segmentation Dice Score with an increase of 1.62%. Compared to prior works using the same dataset, our model achieved the highest Dice Coefficient of 98%, a Jaccard Index of 96.11%, and a Hausdorff Distance of 0.329 mm on the test set. Our lightweight U‐Net, combined with GAN‐based data augmentation, effectively addresses the challenge of data scarcity and enhances the segmentation of the fetal head with precise delineation, providing a robust solution in clinical application for early fetal anomaly detection and prenatal diagnosis.