Accurate segmentation of cervical structures in transvaginal ultrasound (TVS) is critical for assessing the risk of spontaneous preterm birth (PTB), yet the scarcity of labeled data limits the performance of supervised learning approaches. This paper introduces the Fetal Ultrasound Grand Challenge (FUGC), the first benchmark for semi-supervised learning in cervical segmentation, hosted at ISBI 2025. FUGC provides a dataset of 890 TVS images, including 500 training images, 90 validation images, and 300 test images. Methods were evaluated using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and runtime (RT), with a weighted combination of 0.4/0.4/0.2. The challenge attracted 10 teams with 82 participants submitting innovative solutions. The best-performing methods for each individual metric achieved 90.26\% mDSC, 38.88 mHD, and 32.85 ms RT, respectively. FUGC establishes a standardized benchmark for cervical segmentation, demonstrates the efficacy of semi-supervised methods with limited labeled data, and provides a foundation for AI-assisted clinical PTB risk assessment.

翻译：经阴道超声（TVS）中宫颈结构的精确分割对于评估自发性早产（PTB）风险至关重要，然而标注数据的稀缺限制了监督学习方法的性能。本文介绍了胎儿超声大挑战（FUGC），这是首个针对宫颈分割的半监督学习基准，于ISBI 2025会议上发布。FUGC提供了一个包含890张TVS图像的数据集，其中500张训练图像、90张验证图像和300张测试图像。方法评估采用Dice相似系数（DSC）、豪斯多夫距离（HD）和运行时间（RT），权重组合为0.4/0.4/0.2。本次挑战吸引了10支团队共82名参与者提交了创新解决方案。在各单项指标上表现最佳的方法分别达到了90.26% mDSC、38.88 mHD和32.85 ms RT。FUGC为宫颈分割建立了标准化基准，展示了在有限标注数据下半监督方法的有效性，并为人工智能辅助的临床PTB风险评估奠定了基础。