Fetal head segmentation is a crucial step in measuring the fetal head circumference (HC) during gestation, an important biometric in obstetrics for monitoring fetal growth. However, manual biometry generation is time-consuming and results in inconsistent accuracy. To address this issue, convolutional neural network (CNN) models have been utilized to improve the efficiency of medical biometry. But training a CNN network from scratch is a challenging task, we proposed a Transfer Learning (TL) method. Our approach involves fine-tuning (FT) a U-Net network with a lightweight MobileNet as the encoder to perform segmentation on a set of fetal head ultrasound (US) images with limited effort. This method addresses the challenges associated with training a CNN network from scratch. It suggests that our proposed FT strategy yields segmentation performance that is comparable when trained with a reduced number of parameters by 85.8%. And our proposed FT strategy outperforms other strategies with smaller trainable parameter sizes below 4.4 million. Thus, we contend that it can serve as a dependable FT approach for reducing the size of models in medical image analysis. Our key findings highlight the importance of the balance between model performance and size in developing Artificial Intelligence (AI) applications by TL methods. Code is available at https://github.com/13204942/FT_Methods_for_Fetal_Head_Segmentation.

翻译：胎儿头部分割是妊娠期间测量胎儿头围（HC）的关键步骤，也是产科中监测胎儿生长的重要生物特征参数。然而，手动生成生物特征测量值耗时且精度不一致。为解决这一问题，卷积神经网络（CNN）模型已被用于提高医学生物特征测量的效率。但由于从头训练CNN网络具有挑战性，我们提出了一种迁移学习（TL）方法。该方法通过微调（FT）以轻量级MobileNet为编码器的U-Net网络，在有限标注数据下对一组胎儿头部超声（US）图像进行分割。该策略解决了从头训练CNN网络的难题。研究表明，我们提出的FT策略在参数量减少85.8%的情况下仍能取得可比的图像分割性能。当可训练参数量低于440万时，该策略优于其他微调方案。因此，我们认为该方法可作为医学图像分析中减少模型规模的有效FT方案。关键发现揭示了通过迁移学习方法开发人工智能（AI）应用时，模型性能与规模平衡的重要性。代码开源地址：https://github.com/13204942/FT_Methods_for_Fetal_Head_Segmentation