首个宫颈分割半监督学习基准，助力早产风险评估
FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation
- 构建首个宫颈超声半监督学习评测基准，含890张图像
- 最佳方法达90.26% Dice、38.88mm Hausdorff距离、32.85ms推理时间
- 为临床早产风险预测提供可复现的AI评估框架
经阴道超声（TVS）中宫颈结构的精准分割对评估自发性早产（PTB）风险至关重要，但标注数据稀缺限制了监督学习性能。本文提出胎儿超声大挑战（FUGC），首个面向宫颈分割的半监督学习基准，于ISBI 2025举办。FUGC提供包含890张TVS图像的数据集，其中训练集500张、验证集90张、测试集300张。方法评估采用Dice相似系数（DSC）、豪斯多夫距离（HD）和运行时（RT），加权组合为0.4/0.4/0.2。共10支团队、82名参与者提交创新方案。各指标最优结果分别为：90.26%平均DSC、38.88mm平均HD、32.85ms RT。FUGC建立了宫颈分割标准化评测基准，验证了有限标注数据下半监督方法的有效性，为基于AI的临床早产风险评估奠定基础。
原文摘要 · Abstract (English)
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.
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