A Dataset for Fetal Ultrasound Grand Challenge: Semi-Supervised Cervical Segmentation
Bai, Jieyun

@article{zenodo_16893174,
  title= {A Dataset for Fetal Ultrasound Grand Challenge: Semi-Supervised Cervical Segmentation},
  journal= {},
  author= {Bai, Jieyun},
  year= {2025},
  url= {https://zenodo.org/records/16893174},
  abstract= {The dataset includes 890 TVS images in total: 500 for training (50 labeled and 450 unlabeled), 90 for validation, and 300 for testing. Annotations for the anterior and posterior lips were generated through an automatic segmentation step followed by manual correction by an experienced radiologist using the Pair software.

**License:** https://creativecommons.org/licenses/by/4.0/. **Source:** https://zenodo.org/records/16893174

**Related papers:**
- Bai, J., Tang, Y., Zhou, Z., et al. FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation. IEEE Transactions on Medical Imaging (2026). arXiv:2601.15572 [doi:10.1109/TMI.2026.3666364](https://doi.org/10.1109/TMI.2026.3666364) · [Semantic Scholar](https://www.semanticscholar.org/paper/8454d30556ac284dd5509aa15c2931bc36c7b9bc)},
  files= {FUGC (Dataset).zip},
  keywords= {},
  license= {https://creativecommons.org/licenses/by/4.0/},
}