IEEE J Biomed Health Inform. 2023 Oct;27(10):4914-4925. doi: 10.1109/JBHI.2023.3298096. Epub 2023 Oct 5.
Ultrasound based estimation of fetal biometry is extensively used to diagnose prenatal abnormalities and to monitor fetal growth, for which accurate segmentation of the fetal anatomy is a crucial prerequisite. Although deep neural network-based models have achieved encouraging results on this task, inevitable distribution shifts in ultrasound images can still result in severe performance drop in real world deployment scenarios. In this article, we propose a complete ultrasound fetal examination system to deal with this troublesome problem by repairing and screening the anatomically implausible results. Our system consists of three main components: A routine segmentation network, a fetal anatomical key points guided repair network, and a shape-coding based selective screener. Guided by the anatomical key points, our repair network has stronger cross-domain repair capabilities, which can substantially improve the outputs of the segmentation network. By quantifying the distance between an arbitrary segmentation mask to its corresponding anatomical shape class, the proposed shape-coding based selective screener can then effectively reject the entire implausible results that cannot be fully repaired. Extensive experiments demonstrate that our proposed framework has strong anatomical guarantee and outperforms other methods in three different cross-domain scenarios.
基于超声的胎儿生物测量估计被广泛用于诊断产前异常和监测胎儿生长，而准确分割胎儿解剖结构是关键前提。尽管基于深度神经网络的模型在这项任务上取得了令人鼓舞的结果，但超声图像中不可避免的分布偏移仍然会导致在实际部署场景中性能严重下降。在本文中，我们提出了一个完整的超声胎儿检查系统，通过修复和筛选不合常理的结果来处理这个麻烦的问题。我们的系统由三个主要部分组成：常规分割网络、基于胎儿解剖关键点的修复网络和基于形状编码的选择性筛选器。我们的修复网络受解剖关键点的指导，具有更强的跨域修复能力，这可以大大提高分割网络的输出。通过量化任意分割掩码与其对应的解剖形状类之间的距离，所提出的基于形状编码的选择性筛选器可以有效地拒绝无法完全修复的整个不合理结果。广泛的实验表明，我们提出的框架具有很强的解剖保证，并在三个不同的跨域场景中优于其他方法。
