Item

Beyond benchmarks of IUGC: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos

Bai, Jieyun
; 
Zhou, Zihao
; 
Tang, Yitong
; 
Gan, Jie
; 
Liang, Zhuonan
; 
Fan, Jianan
; 
Mcguire, Lisa B
; 
Clarke, Jillian L
; 
Cai, Weidong
; 
Spurway, Jacaueline
... show 10 more
Author
Bai, Jieyun
Zhou, Zihao
Tang, Yitong
Gan, Jie
Liang, Zhuonan
Fan, Jianan
Mcguire, Lisa B
Clarke, Jillian L
Cai, Weidong
Spurway, Jacaueline
Tan, Yubo
Wang, Shiye
Shen, Wenda
Yu, Wangwang
Li, Yihao
Zhang, Philippe
Jiang, Weili
Li, Yongjie
Al Nasi, Salem Muhsin Ali Binqahal
Abzhanov, Arsen
Saeed, Numan
Yaqub, Mohammad
Xia, Zunhui
Li, Hongxing
Lan, Libin
Ramesh, Jayroop
Bacher, Valentin
Eid, Mark
Kalabizadeh, Hoda
Rupprecht, Christian
Namburete, Ana IL
Yeung, Pak-Hei
Wyburd, Madeleine K
Dinsdale, Nicola K
Serikbey, Assanali
Li, Jiankai
Chen, Sung-Liang
Hu, Zicheng
Liu, Nana
Deng, Yian
Hu, Wei
Tan, Cong
Zhang, Wenfeng
Nhi, Mai Tuyet
Koehler, Gregor
Stock, Rapheal
Maier-Hein, Klaus
Elbatel, Marawan
Li, Xiaomeng
Slimani, Saad
Campello, Victor M
Ohene-Botwe, Benard
Khobo, Isaac
Huang, Yuxin
Han, Zhenyan
Hou, Hongying
Qiu, Di
Zheng, Zheng
Luo, Gongning
Ni, Dong
Lu, Yaosheng
Lekadir, Karim
Li, Shuo
Supervisor
Department
Computer Vision
Embargo End Date
Type
Journal article
Date
License
Language
English
Collections
Publications
Show all metadata
Research Projects
Organizational Units
Journal Issue
Abstract
A significant proportion (45%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, particularly prevalent in low- and middle-income countries. Intrapartum biometry plays a crucial role in monitoring labor progress. However, the routine use of ultrasound in resource-limited settings is hindered by a shortage of trained sonographers. To tackle this issue, the Intrapartum Ultrasound Grand Challenge (IUGC), co-hosted with MICCAI 2024, was launched. The IUGC designed a multi-task automatic measurement framework oriented towards clinical applications. This framework integrates standard plane classification, fetal head-pubic symphysis segmentation, and biometry, enabling algorithms to leverage complementary information for more accurate estimations. Moreover, the challenge introduced the largest multi-center intrapartum ultrasound video dataset, consisting of 774 videos (68,106 images) collected from three hospitals. This rich dataset provides a solid foundation for algorithm training and evaluation. In this study, we elaborate on the details of the challenge, review the works submitted by eight teams, and interpret their methods from five aspects: preprocessing, data augmentation, learning strategy, model architecture, and post-processing. Additionally, we analyze the results considering various factors to identify key obstacles, explore potential solutions, and highlight ongoing challenges for future research. We conclude that although promising results have been achieved, the research remains in its early stages, and further in-depth exploration is required before clinical implementation. The solutions and the complete dataset are publicly accessible, aiming to drive continuous advancements in automatic biometry for intrapartum ultrasound imaging.
Citation
J. Bai, Z. Zhou, Y. Tang, J. Gan, Z. Liang, J. Fan , et al., "Beyond benchmarks of IUGC: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos," Medical Image Analysis, vol. 111, pp. 104043-104043, 2026, https://doi.org/10.1016/j.media.2026.104043.
Source
Medical Image Analysis
Conference
Keywords
32 Biomedical and Clinical Sciences, 3215 Reproductive Medicine, 3 Good Health and Well Being
Subjects
Source
Publisher
Elsevier
DOI
10.1016/j.media.2026.104043
Additional links
https://www.sciencedirect.com/science/article/pii/S136184152600112X
Full-text link