MICCAI 2026 Challenge on Foundation Model for Ultrasound Biometry
Published in Bioengineering & Biotechnology
🚀 Call for Participation | MICCAI 2026 FU_Biometry Generalizable Ultrasound Biometry Challenge
đź’ˇ A comprehensive benchmark for generalizable ultrasound foundation models, covering prenatal, intrapartum, pediatric, and adult cardiac scenarios, with 20+ AI-based biometric measurement tasks across multi-center datasets.
🎯 Core Tasks
- Multi-center, multi-device ultrasound landmark detection
- Automated biometric measurement for prenatal, intrapartum, pediatric, and adult cardiac ultrasound
- Improving measurement accuracy and cross-device generalization
🏆 Awards
- Champion, Runner-up, Third Place, plus Innovation Award and Best Method Description Award
- Certificates and cash prizes
- Outstanding papers invited to submit to the IEEE TBME Special Issue
- Teams with valid submissions may co-author a review article in Medical Image Analysis
đź“… Important Dates
- April 15 – Training data release
- June 1 – Validation data release
- August 1 – Test data release
- August 15 – Submission deadline
👉 Register here:
https://www.codabench.org/competitions/15590/
🙌 We warmly welcome researchers, clinicians, and students to participate.
Please feel free to share this challenge with colleagues and collaborators!
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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