PSFHS 挑战报告:从产后超声图像中分割耻骨联合和胎儿头部

IF 10.7 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

摘要

对胎儿和母体结构进行分割,尤其是国际妇产科超声学会(ISUOG)所倡导的用于监测产程进展的产前超声成像,是定量诊断和临床决策的关键第一步。这需要产科专业人员进行专业分析,这项工作 i) 非常耗费时间和成本,ii) 经常产生不一致的结果。自动分割算法在生物测量中的实用性已得到证实,但现有结果仍不理想。为了推动这一领域的发展,在第 26 届国际医学影像计算和计算机辅助干预会议(MICCAI 2023)召开的同时,还举办了耻骨联合-胎儿头部分割(PSFHS)大挑战。该挑战赛旨在加强国际范围内自动分割算法的开发,提供了迄今为止最大的数据集,包括从两家机构的三家医院的两台超声波机上收集的 5,101 张产后超声图像。由于科学界的踊跃参与,在初赛阶段从 193 名报名者的 179 个参赛项目中选出了前 8 名进入第二阶段。这些算法提升了产前超声图像自动 PSFHS 的技术水平。对结果的全面分析指出了该领域目前面临的挑战,并概述了对未来工作的建议。优秀的解决方案和完整的数据集将继续公开,以促进产前超声成像的自动分割和生物测量技术的进一步发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images

PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images
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.
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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