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IUGC: A Benchmark of Landmark Detection in End-to-End Intrapartum Ultrasound Biometry

Bai, Jieyun
; 
Tang, Yitong
; 
Liu, Xiao
; 
Hu, Jiale
; 
Li, Yunda
; 
Chen, Xufan
; 
Wang, Yufeng
; 
Ma, Chen
; 
Li, Yunshu
; 
Guo, Bowen
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Author
Bai, Jieyun
Tang, Yitong
Liu, Xiao
Hu, Jiale
Li, Yunda
Chen, Xufan
Wang, Yufeng
Ma, Chen
Li, Yunshu
Guo, Bowen
Jiao, Jing
Huang, Yi
Wang, Kun
Li, Lifei
Ma, Yuzhang
Han, Xiaoxin
Shao, Haochen
Yang, Zi
Liu, Qingchen
Hu, Yuchen
Kuang, Jingfan
Song, Shanglin
Krishna, Anirvan
Khan, Zaid Ahmed
Li, Zelan
Zhang, Zhengyang
Zhang, Hansen
Cheng, Yan
Zhang, Xuezhi
Chen, Xi
Yan, Hao
Tong, Lyuyang
Du, Bo
Deng, Bo
Chen, Yu
Peng, Zilun
Rezaei, Saeid
Gan, Jie
Cai, Weidong
Wang, Fangyijie
Curran, Kathleen M
Silvestre, Guénolé
Khobo, Isaac
Lu, Yaosheng
Ni, Dong
Huang, Yuxin
Yaqub, Mohammad
Ma, Jun
Lekadir, Karim
Li, Shuo
Supervisor
Department
Computer Vision
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Journal article
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Language
English
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Abstract
Accurate intrapartum biometry plays a crucial role in monitoring labor progression and preventing complications. However, its clinical application is limited by challenges such as the difficulty in identifying anatomical landmarks and the variability introduced by operator dependency. To overcome these challenges, the Intrapartum Ultrasound Grand Challenge (IUGC) 2025, in collaboration with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), was organized to accelerate the development of automatic measurement techniques for intrapartum ultrasound analysis. The challenge featured a large-scale, multi-center dataset comprising over 32,000 images from 24 hospitals and research institutes. These images were annotated with key anatomical landmarks of the pubic symphysis (PS) and fetal head (FH), along with the corresponding biometric parameter-the angle of progression (AoP). Ten participating teams proposed a variety of end-to-end and semi-supervised frameworks, incorporating advanced strategies such as foundation model distillation, pseudo-label refinement, anatomical segmentation guidance, and ensemble learning. A comprehensive evaluation revealed that the winning team achieved superior accuracy, with a Mean Radial Error (MRE) of 6.53 ± 4.38 pixels for the right PS landmark, 8.60 ± 5.06 pixels for the left PS landmark, 19.90 ± 17.55 pixels for the FH tangent landmark, and an absolute AoP difference of 3.81 ± 3.12°. This top-performing method demonstrated accuracy comparable to expert sonographers, emphasizing the clinical potential of automated intrapartum ultrasound analysis. However, challenges remain, such as the trade-off between accuracy and computational efficiency, the lack of segmentation labels and video data, and the need for extensive multi-center clinical validation. IUGC 2025 thus sets the first benchmark for landmark-based intrapartum biometry estimation and provides an open platform for developing and evaluating real-time, intelligent ultrasound analysis solutions for labor management.
Citation
Source
Medical Image Analysis
Conference
Keywords
32 Biomedical and Clinical Sciences, 40 Engineering
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Source
Publisher
Elsevier
DOI
10.1016/j.media.2026.103960
Additional links
https://www.sciencedirect.com/science/article/pii/S1361841526000290?via%3Dihub
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