Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Jinan University, Guangzhou, China; Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Jinan University, Guangzhou, China.
Med Image Anal. 2025 Jan;99:103353. doi: 10.1016/j.media.2024.103353. Epub 2024 Sep 21.
Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. This requires specialized analysis by obstetrics professionals, in a task that i) is highly time- and cost-consuming and ii) often yields inconsistent results. The utility of automatic segmentation algorithms for biometry has been proven, though existing results remain suboptimal. To push forward advancements in this area, the Grand Challenge on Pubic Symphysis-Fetal Head Segmentation (PSFHS) was held alongside the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5,101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions. The scientific community's enthusiastic participation led to the selection of the top 8 out of 179 entries from 193 registrants in the initial phase to proceed to the competition's second stage. These algorithms have elevated the state-of-the-art in automatic PSFHS from intrapartum ultrasound images. A thorough analysis of the results pinpointed ongoing challenges in the field and outlined recommendations for future work. The top solutions and the complete dataset remain publicly available, fostering further advancements in automatic segmentation and biometry for intrapartum ultrasound imaging.
胎儿和母体结构的分割，特别是国际妇产科超声学会（ISUOG）倡导的产时超声成像，用于监测分娩进展，是进行定量诊断和临床决策的关键第一步。这需要妇产科专业人员进行专门的分析，这项任务具有以下两个特点：i）高度耗时和昂贵，ii）结果往往不一致。自动分割算法在生物测量中的应用已经得到证实，尽管现有结果仍不尽如人意。为了推动这一领域的进展，耻骨联合-胎儿头部分割（PSFHS）大型挑战赛与第 26 届国际医学影像计算和计算机辅助干预会议（MICCAI 2023）同期举行。该挑战赛旨在促进国际范围内自动分割算法的发展，提供了迄今为止最大的数据集，其中包含来自两个机构的三家医院的两台超声机采集的 5101 张产时超声图像。该挑战赛得到了科学界的热烈响应，从 193 名注册者的 179 个参赛作品中，有 8 个作品进入了初始阶段的第二轮比赛。这些算法将自动 PSFHS 算法的最新水平提升到了一个新的高度。对结果的深入分析指出了该领域的持续挑战，并为未来的工作提出了建议。顶级解决方案和完整数据集仍然公开可用，这将促进产时超声成像中自动分割和生物测量的进一步发展。
