标题： 通过跨种群增强实现不同孕周的鲁棒胎儿姿态估计 显示英文标题

标题： Robust Fetal Pose Estimation across Gestational Ages via Cross-Population Augmentation

摘要： 胎儿运动是神经发育和宫内健康的重要指标，但其量化仍然具有挑战性，尤其是在较早的妊娠年龄（GA）时。当前的方法通过预测3D回波平面成像（EPI）时间序列上的标记点位置来跟踪胎儿运动，主要针对第三孕期的胎儿。预测的标记点使得胎儿身体的简化成为可能，便于后续分析。尽管这些方法在其训练年龄分布范围内表现良好，但由于在妊娠过程中母亲和胎儿都会发生显著的解剖变化，以及获取标注的早期GA EPI数据的难度，它们在早期GA上始终无法推广。在本工作中，我们开发了一种跨人群的数据增强框架，仅使用来自较老GA人群的标注图像，使姿态估计模型能够稳健地推广到更年轻的GA临床队列。具体而言，我们引入了一种胎儿特定的数据增强策略，模拟早期GA的独特宫内环境和胎儿位置。我们的实验发现，跨人群增强在较老GA和具有挑战性的早期GA病例中均显著减少了变异性并提高了性能。通过在妊娠期间实现更可靠的姿态估计，我们的工作有可能促进困难的4D胎儿成像环境中早期的临床检测和干预。代码可在https://github.com/sebodiaz/cross-population-pose获取。 显示英文摘要

摘要： Fetal motion is a critical indicator of neurological development and intrauterine health, yet its quantification remains challenging, particularly at earlier gestational ages (GA). Current methods track fetal motion by predicting the location of annotated landmarks on 3D echo planar imaging (EPI) time-series, primarily in third-trimester fetuses. The predicted landmarks enable simplification of the fetal body for downstream analysis. While these methods perform well within their training age distribution, they consistently fail to generalize to early GAs due to significant anatomical changes in both mother and fetus across gestation, as well as the difficulty of obtaining annotated early GA EPI data. In this work, we develop a cross-population data augmentation framework that enables pose estimation models to robustly generalize to younger GA clinical cohorts using only annotated images from older GA cohorts. Specifically, we introduce a fetal-specific augmentation strategy that simulates the distinct intrauterine environment and fetal positioning of early GAs. Our experiments find that cross-population augmentation yields reduced variability and significant improvements across both older GA and challenging early GA cases. By enabling more reliable pose estimation across gestation, our work potentially facilitates early clinical detection and intervention in challenging 4D fetal imaging settings. Code is available at https://github.com/sebodiaz/cross-population-pose.