Fetal pose estimation in 3D ultrasound (US) involves identifying a set of
associated fetal anatomical landmarks. Its primary objective is to provide
comprehensive information about the fetus through landmark connections, thus
benefiting various critical applications, such as biometric measurements, plane
localization, and fetal movement monitoring. However, accurately estimating the
3D fetal pose in US volume has several challenges, including poor image
quality, limited GPU memory for tackling high dimensional data, symmetrical or
ambiguous anatomical structures, and considerable variations in fetal poses. In
this study, we propose a novel 3D fetal pose estimation framework (called
FetusMapV2) to overcome the above challenges. Our contribution is three-fold.
First, we propose a heuristic scheme that explores the complementary network
structure-unconstrained and activation-unreserved GPU memory management
approaches, which can enlarge the input image resolution for better results
under limited GPU memory. Second, we design a novel Pair Loss to mitigate
confusion caused by symmetrical and similar anatomical structures. It separates
the hidden classification task from the landmark localization task and thus
progressively eases model learning. Last, we propose a shape priors-based
self-supervised learning by selecting the relatively stable landmarks to refine
the pose online. Extensive experiments and diverse applications on a
large-scale fetal US dataset including 1000 volumes with 22 landmarks per
volume demonstrate that our method outperforms other strong competitors.

本研究提出了一种名为 FetusMapV2 的新型三维胎儿姿势估计框架，通过探索互补网络结构、限制 GPU 内存等方法来克服三维胎儿姿势估计的挑战，设计了一种新的损失函数来解决对称和相似解剖结构造成的混淆问题，并提出基于形状先验的自监督学习以在线优化姿势。在大规模胎儿超声数据集上进行的大量实验证明，我们的方法优于其他竞争对手。