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

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.