文献类型：会议论文

英文题名：SSL-FetalBioNet: Self-supervised Learning for Automated Angle of Progression Measurement in Intrapartum Ultrasound

作者：Li Lifei[1];Ma Yuzhang[1];Han Xiaoxin[1];Shao Haochen[1];Wang Kun[1,1]

第一作者：李林芳

通信作者：Wang, K[1]

机构：[1]Gansu Univ Chinese Med, Lanzhou 730000, Gansu, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Univ Chinese Med, Lanzhou 730000, Gansu, Peoples R China.|[10735]甘肃中医药大学;

会议论文集：28th International Conference on Medical Image Computing and Computer Assisted Intervention-MICCAI-Annual

会议日期：SEP 23-27, 2025

会议地点：Daejeon, SOUTH KOREA

语种：英文

外文关键词：Self-supervised Learning; U-Net; Intrapartum Ultrasound; Angle of Progress

年份：2026

摘要：During childbirth, real-time assessment of fetal head position and progression is crucial for ensuring the safety of both mother and infant. Detecting key anatomical landmarks in intrapartum ultrasound images and calculating the Angle of progression (AoP) have become critical techniques in the next-generation childbirth monitoring protocol proposed by the World Health Organization (WHO). However, traditional manual analysis is time-consuming and prone to subjective bias, highlighting the urgent need for automated methods to achieve standardized and precise childbirth assessment. This paper presents a key point detection approach combining self-supervised pre-training with a U-Net architecture: first, the encoder is pre-trained using large-scale unlabeled images through self-supervision to uncover latent structural information; subsequently, this pre-trained encoder is transferred to the supervised learning stage to achieve precise localization of three key points (PS1, PS2, FH1). Our method achieved eighth place in the Intrapartum Ultrasound Grand Challenge 2025, demonstrating its effectiveness and generalization capability in the task of key point detection in intrapartum ultrasound. This work provides a practical and feasible pathway toward automated and scalable childbirth monitoring, with significant implications for global maternal and infant health.