Accurate measurement of fetal head circumference is crucial for estimating fetal growth during routine prenatal screening. Prior to measurement, it is necessary to accurately identify and segment the region of interest, specifically the fetal head, in ultrasound images. Recent advancements in deep learning techniques have shown significant progress in segmenting the fetal head using encoder-decoder models. Among these models, U-Net has become a standard approach for accurate segmentation. However, training an encoder-decoder model can be a time-consuming process that demands substantial computational resources. Moreover, fine-tuning these models is particularly challenging when there is a limited amount of data available. There are still no "best-practice" guidelines for optimal fine-tuning of U-net for fetal ultrasound image segmentation. This work summarizes existing fine-tuning strategies with various backbone architectures, model components, and fine-tuning strategies across ultrasound data from Netherlands, Spain, Malawi, Egypt and Algeria. Our study shows that (1) fine-tuning U-Net leads to better performance than training from scratch, (2) fine-tuning strategies in decoder are superior to other strategies, (3) network architecture with less number of parameters can achieve similar or better performance. We also demonstrate the effectiveness of fine-tuning strategies in low-resource settings and further expand our experiments into few-shot learning. Lastly, we publicly released our code and specific fine-tuned weights.

翻译：胎儿头围的精确测量对于常规产前筛查中评估胎儿生长至关重要。在进行测量前，需在超声图像中准确识别并分割感兴趣区域，即胎儿头部。深度学习技术的最新进展显示，基于编码器-解码器模型在胎儿头部分割方面取得显著进步。其中U-Net已成为实现精确分割的标准方法。然而，训练编码器-解码器模型是耗时且需要大量计算资源的过程。此外，当可用数据量有限时，微调这些模型尤为困难。目前针对胎儿超声图像分割的U-Net优化微调仍缺乏"最佳实践"指南。本研究系统总结了基于荷兰、西班牙、马拉维、埃及和阿尔及利亚超声数据的多种骨干架构、模型组件及微调策略。实验表明：(1) 微调U-Net相比从头训练能获得更优性能，(2) 解码器微调策略优于其他策略，(3) 参数较少的网络架构可获得相当或更好的性能。我们同时验证了低资源环境下微调策略的有效性，并将实验拓展至少样本学习场景。最后，我们公开了代码及特定微调权重。