文献类型：期刊文献

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

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

第一作者：Kun, Wang

机构：[1] Gansu University of Chinese medicine, Gansu, Lanzhou, 730000, China

第一机构：甘肃中医药大学

年份：2026

卷号：16317 LNCS

起止页码：24

外文期刊名：Lecture Notes in Computer Science

收录：EI(收录号：20260419970786)

语种：英文

外文关键词：Automation - Fetal monitoring - Neonatal monitoring - Obstetrics - Self-supervised learning - Signal encoding - Supervised learning - Ultrasonic applications

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. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.