FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation

TL;DR

The paper introduces FUGC, a benchmark dataset and challenge for semi-supervised cervical segmentation in ultrasound images, demonstrating the effectiveness of semi-supervised methods with limited labeled data for clinical risk assessment.

Contribution

It establishes the first benchmark for semi-supervised cervical segmentation in ultrasound images, providing a dataset, evaluation metrics, and a competitive platform.

Findings

Top methods achieved over 90% mDSC

Semi-supervised methods outperform supervised ones with limited data

Benchmark facilitates future research in AI-assisted PTB risk assessment

Abstract

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.