FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 44 references that are in the map. First posted on arXiv 22 Jan 2026.
Builds on
11 of 44 references.
References · 11
- 2019BIAS: Transparent reporting of biomedical image analysis challengescited by 57
- 2024UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentationcited by 54
- 2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52
- 2022A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysiscited by 29
- 2024From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learningcited by 15
- 2024Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Surveycited by 12
- 2024Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learningcited by 9
- 2024PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Imagescited by 8
- 2019Estimation of preterm birth markers with U-Net segmentation networkcited by 7
- 2020Spontaneous preterm birth prediction using convolutional neural networkscited by 5
- 2025ERSR: An Ellipse-constrained pseudo-label refinement and symmetric regularization framework for semi-supervised fetal head segmentation in ultrasound imagescited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.