Jieyun Bai's Paper "Maternal-Fetal Ultrasound Video Dataset for End-to-End Intrapartum Biometry and Multi-task Learning "
Published in Bioengineering & Biotechnology
We are thrilled to share the public release of the Maternal-Fetal Ultrasound Video Dataset for End-to-End Intrapartum Biometry and Multi-task Learning (Published in Scientific Data, DOI: 10.1038/s41597-026-06900-5
)! This marks the first-ever multi-center, multi-device, and multi-category labeled intrapartum ultrasound video dataset, designed to drive progress in AI-based maternal and fetal health solutions.
đź“… Key Highlights:
774 ultrasound videos and 68,106 images collected across three leading institutions.
Annotations for standard plane classification, segmentation of pubic symphysis and fetal head, and ultrasound-derived biometric parameters (Angle of Progression, Head-Symphysis Distance).
Designed to enhance multi-task learning and support the development of fully automated intrapartum ultrasound parameter measurement.
This dataset represents a crucial leap forward in improving the accuracy and efficiency of labor monitoring systems and empowering automated decision-making in obstetrics.
đź’ˇ Why it Matters:
AI-driven clinical decision support for real-time intrapartum assessments.
Streamlines automated workflows for monitoring fetal head descent and labor progression.
Vital for advancing multi-task learning in medical imaging.
đź”— Access the Dataset: https://lnkd.in/gRR7PhFm
đź“„ Published Article: Maternal-Fetal Ultrasound Video Dataset
I am an Associate Professor at Jinan University, a Joint Appointed Professor at the First Affiliated Hospital of Jinan University, and the Chief Scientist for Medical Artificial Intelligence at Guangzhou Lianyin Medical Technology Co., Ltd.
I was a postdoctoral researcher at the Auckland Bioengineering Institute, University of Auckland, working with Prof. Jichao Zhao from 2018 to 2019. I received my Ph.D. in Computer Science and Technology from Harbin Institute of Technology in 2017, under the supervision of Prof. Kuanquan Wang and Prof. Henggui Zhang, and I was also a visiting scholar at the Auckland Bioengineering Institute for one year.
My research focuses on AI-driven medical digital twins, ultrasound-based maternal–fetal monitoring, multimodal biomedical signal processing, and clinical decision support systems. I have published extensively on deep learning–based medical image and signal analysis in leading international conferences, including MICCAI, ISBI, EMBC, BIBM, and CinC, as well as in top-tier journals such as Medical Image Analysis (MIA), IEEE Transactions on Medical Imaging (TMI), IEEE Journal of Biomedical and Health Informatics (JBHI), and PLOS Computational Biology.
My team is dedicated to translational and data-driven medical AI research, leveraging large-scale clinical data collected from multi-center hospital systems. We actively develop open datasets, benchmark challenges, and open-source platforms to support reproducible and clinically relevant AI research.
I am a leading organizer in the international ultrasound AI community and currently serve as an Area Chair for MICCAI and ISBI Challenges. Through these initiatives, I have coordinated global collaborations among academia, hospitals, and industry, and have released large-scale, clinically curated datasets and standardized evaluation platforms to the community.
We are strongly committed to open science and have released more than 10 public datasets and code repositories on platforms such as Kaggle and Zenodo.
My group is currently recruiting postdoctoral fellows, research scientists, and graduate students in medical image analysis, medical digital twins, and AI-enabled maternal and cardiovascular healthcare.
Researchers and clinicians interested in medical AI and translational biomedical engineering are warmly welcome to contact me.
Website: https://jieyunbai.github.io/
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Scientific Data
A peer-reviewed, open-access journal for descriptions of datasets, and research that advances the sharing and reuse of scientific data.
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