Materal-Fetal Ultrasound Video Dataset for End-to-end Intrapartum Biometry and Multi-task Learning
Jieyun, Bai

@article{zenodo_17655183,
  title= {Materal-Fetal Ultrasound Video Dataset for End-to-end Intrapartum Biometry and Multi-task Learning},
  journal= {},
  author= {Jieyun, Bai},
  year= {2025},
  url= {https://zenodo.org/records/17655183},
  abstract= {Intrapartum biometry is of vital significance in monitoring labor progress. However, the realization of AI-based end-to-end intrapartum biometry and labor progress assessment requires intrapartum ultrasound video datasets with multi - category annotations, and currently, there is no such public dataset available. To bridge this gap, we have publicly released, for the first time, a multi-center, multi-device, and multi-category labeled intrapartum ultrasound dataset. This dataset comprises 774 videos / 68,106 images, along with corresponding standard plane classification labels, multi-class segmentation labels of pubic symphysis and fetal head, and two ultrasound parameter labels that characterize labor progress. This dataset can facilitate research on multi-task learning methods and the development of end-to-end automated approaches, especially in the automation of obstetric processes and auxiliary decision - making.

**License:** https://creativecommons.org/licenses/by/4.0/. **Source:** https://zenodo.org/records/17655183

**Related papers:**
- Bai, J., et al. Maternal-Fetal Ultrasound Video Dataset for End-to-end Intrapartum Biometry and Multi-task Learning. Scientific Data (2026). [doi:10.1038/s41597-026-06900-5](https://doi.org/10.1038/s41597-026-06900-5) · [Semantic Scholar](https://www.semanticscholar.org/paper/0a0a7443c91ece7cac1471f6a1f83df21216bf23)
- Bai, J., Zhou, Z., Tang, Y., et al. Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos. Medical Image Analysis 111, 104043 (2026). arXiv:2602.12922 [doi:10.1016/j.media.2026.104043](https://doi.org/10.1016/j.media.2026.104043) · [Semantic Scholar](https://www.semanticscholar.org/paper/e6d9cfdb355b6bcec8095e92d14373dc8bcac7c8)},
  files= {DatasetV3.zip},
  keywords= {Intrapartum Ultrasound, Fetal Ultrasound, Point-of-care Ultrasound, Fetal Biometry, Ultrasound Standard Plane Detection, Ultrasound Segmentation, Multi-task Learning, Semi-Supervised Learning, Foundational Model, Segment Anything Model},
  license= {https://creativecommons.org/licenses/by/4.0/},
}