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FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation

Bai, Jieyun
; 
Tang, Yitong
; 
Zhou, Zihao
; 
Islam, Mahdi
; 
Tabassum, Musarrat
; 
Almar-Munoz, Enrique
; 
Liu, Hongyu
; 
Meng, Hui
; 
Lv, Nianjiang
; 
Deng, Bo
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Author
Bai, Jieyun
Tang, Yitong
Zhou, Zihao
Islam, Mahdi
Tabassum, Musarrat
Almar-Munoz, Enrique
Liu, Hongyu
Meng, Hui
Lv, Nianjiang
Deng, Bo
Chen, Yu
Peng, Zilun
Xiao, Yusong
Xiao, Li
Tran, Nam-Khanh
Phan-Le, Dac-Phu
Nguyen, Hai-Dang
Liu, Xiao
Hu, Jiale
Huang, Mingxu
Liang, Jitao
Feng, Chaolu
Zhang, Xuezhi
Tong, Lyuyang
Du, Bo
Pham, Ha-Hieu
Nguyen, Thanh-Huy
Xu, Min
Jiang, Juntao
Zhang, Jiangning
Liu, Yong
Hasan, Md Kamrul
Gan, Jie
Liang, Zhuonan
Cai, Weidong
Huang, Yuxin
Luo, Gongning
Yaqub, Mohammad
Lekadir, Karim
Supervisor
Department
Computer Vision
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Journal article
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Language
English
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Abstract
Accurate segmentation of cervical structures in transvaginal ultrasound (TVS) is critical for assessing the risk of spontaneous preterm birth (PTB), yet the scarcity of labeled data limits the performance of supervised learning approaches. This paper introduces the Fetal Ultrasound Grand Challenge (FUGC), the first benchmark for semi-supervised learning in cervical segmentation, hosted at ISBI 2025. FUGC provides a dataset of 890 TVS images, including 500 training images, 90 validation images, and 300 test images. Methods were evaluated using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and runtime (RT), with a weighted combination of 0.4/0.4/0.2. The challenge attracted 10 teams with 82 participants submitting innovative solutions. The best-performing methods for each individual metric achieved 90.26% mDSC, 38.88 mHD, and 32.85 ms RT, respectively. FUGC establishes a standardized benchmark for cervical segmentation, demonstrates the efficacy of semi-supervised methods with limited labeled data, and provides a foundation for AI-assisted clinical PTB risk assessment.
Citation
J. Bai, Y. Tang, Z. Zhou, M. Islam, M. Tabassum, E. Almar-Munoz , et al., "FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation," IEEE Transactions on Medical Imaging, vol. PP, no. 99, pp. 1-1, 2026, https://doi.org/10.1109/tmi.2026.3666364.
Source
IEEE Transactions on Medical Imaging
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Keywords
40 Engineering, 46 Information and Computing Sciences
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Publisher
IEEE
DOI
10.1109/tmi.2026.3666364
Additional links
https://ieeexplore.ieee.org/document/11400574
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