Fetal Ultrasound Grand Challenge: Semi-Supervised Cervical Segmentation (FUGC25)
Creators: name: Bai, Jieyun
name: Yang, Ziduo
name: Hasan, Kamrul
name: Gan, Jie
name: Liang, Zhuonan
name: Cai, Weidong
name: Tan, Tao
name: Ye, Jing
name: Yaqub, Mohammad
name: Ni, Dong
name: Slimani, Saad
name: Ohene-Botwe, Benard
name: Roman Victor Manuel, Campello
name: Lekadir, Karim
Publisher: name: Zenodo
Year: 2024
Subjects: Preterm birth
Cervix
Segmentation
fetal ultrasound
Semi-supervised learning
ISBI 2025 challenge

description: Premature birth remains one of the leading causes of neonatal mortality in the U.S. and other developed nations, posing significant healthcare challenges. Despite numerous efforts, strategies to prevent preterm labor and birth have yielded only limited success. However, recent advancements have improved the ability to predict preterm birth, offering a crucial opportunity for early intervention. Among these advancements, ultrasound imaging of the uterine cervix has emerged as a key clinical tool in predicting spontaneous preterm labor and birth. Early identification of high-risk pregnancies enables healthcare professionals to focus obstetric and neonatal care on those most in need, improving outcomes.From a biomedical perspective, transvaginal ultrasound is the preferred method for visualizing the cervix in most patients, offering detailed insight into cervical anatomy and structure. Accurate segmentation of ultrasound (US) images of the cervical muscles is essential for analyzing deep muscle structures, assessing their function, and monitoring treatment protocols tailored to individual patients.From a technical standpoint, the manual annotation of cervical structures in transvaginal ultrasound images is labor-intensive and time-consuming, limiting the availability of large labeled datasets required for robust machine learning models. In response to this challenge, semisupervised learning approaches have shown potential by leveraging both labeled and unlabeled data, enabling the extraction of useful information from unannotated cases. This method could reduce the need for extensive manual annotation while maintaining accuracy, thus accelerating the development of automated cervical image segmentation systems. The envisioned impact of this challenge is twofold: improving clinical decision-making through more accessible and accurate diagnostic tools and advancing machine learning techniques for medical image analysis, particularly in resource-constrained environments.We extend the MICCAI PSFHS 2023 Challenge and the MICCAI IUGC 2024 Challenge from fully supervised settings to a semi-supervised setting that focuses on how to use unlabeled data. Specifically, we we provide a small number of labeled cases (50) and a large number of unlabeled cases (450) in the training set, 90 visible cases for validation, and 300 hidden cases for testing.

The FUGC25 challenge has three main features:(1) Task: we use a semi-supervised setting that focuses on how to use unlabeled data.(2) Dataset: we curate a large-scale and diverse ultrasound dataset, including 800+ images obtained with seven different ultrasound machines;(3) Evaluation measures: we not only focus on segmentation accuracy but also segmentation efficiency and resource comsumption.