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<p>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 public video dataset available for multi-category fine-grained classification. While several image datasets exist for related tasks (e.g., JNU-IFM, PSFHS, IUGC), a dedicated benchmark in the video domain remains unavailable. 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.</p>

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Maternal-Fetal Ultrasouno Video Dataset for End-to-end Intrapartum Biometry and Multi-task Learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月23日

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  • Ming Niu,
  • Jieyun Bai,
  • Yunbo Gao,
  • Yitong Tang,
  • Yaosheng Lu,
  • Zhenyan Han,
  • Hongying Hou,
  • Yuxin Huang

摘要

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 public video dataset available for multi-category fine-grained classification. While several image datasets exist for related tasks (e.g., JNU-IFM, PSFHS, IUGC), a dedicated benchmark in the video domain remains unavailable. 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.