A substantial proportion (45\%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, with a particularly high burden in low- and middle-income countries. Intrapartum biometry plays a critical role in monitoring labor progression; however, the routine use of ultrasound in resource-limited settings is hindered by a shortage of trained sonographers. To address this challenge, the Intrapartum Ultrasound Grand Challenge (IUGC), co-hosted with MICCAI 2024, was launched. The IUGC introduces a clinically oriented multi-task automatic measurement framework that integrates standard plane classification, fetal head-pubic symphysis segmentation, and biometry, enabling algorithms to exploit complementary task information for more accurate estimation. Furthermore, the challenge releases the largest multi-center intrapartum ultrasound video dataset to date, comprising 774 videos (68,106 frames) collected from three hospitals, providing a robust foundation for model training and evaluation. In this study, we present a comprehensive overview of the challenge design, review the submissions from eight participating teams, and analyze their methods from five perspectives: preprocessing, data augmentation, learning strategy, model architecture, and post-processing. In addition, we perform a systematic analysis of the benchmark results to identify key bottlenecks, explore potential solutions, and highlight open challenges for future research. Although encouraging performance has been achieved, our findings indicate that the field remains at an early stage, and further in-depth investigation is required before large-scale clinical deployment. All benchmark solutions and the complete dataset have been publicly released to facilitate reproducible research and promote continued advances in automatic intrapartum ultrasound biometry.

翻译：产时阶段（分娩过程）占孕产妇死亡、新生儿死亡和死胎的很大比例（45%），在中低收入国家负担尤为沉重。产时生物测量在监测产程进展中起着关键作用；然而，在资源有限的环境中，由于缺乏经过培训的超声医师，超声的常规使用受到阻碍。为应对这一挑战，与MICCAI 2024联合主办的产时超声重大挑战赛（IUGC）应运而生。IUGC引入了一个面向临床的多任务自动测量框架，该框架整合了标准切面分类、胎头-耻骨联合分割和生物测量，使算法能够利用互补的任务信息进行更准确的估计。此外，该挑战赛发布了迄今为止最大的多中心产时超声视频数据集，包含从三家医院收集的774个视频（68,106帧），为模型训练和评估提供了坚实的基础。在本研究中，我们全面概述了挑战赛的设计，回顾了八个参赛团队的提交方案，并从五个角度分析了他们的方法：预处理、数据增强、学习策略、模型架构和后处理。此外，我们对基准结果进行了系统分析，以识别关键瓶颈、探索潜在解决方案并强调未来研究的开放挑战。尽管已取得令人鼓舞的性能，但我们的研究结果表明该领域仍处于早期阶段，在大规模临床部署之前需要进一步深入研究。所有基准解决方案和完整数据集均已公开发布，以促进可重复研究并推动自动产时超声生物测量技术的持续进步。