PSFHS 挑战报告:从产内超声图像中分割耻骨联合和胎儿头部

Jieyun Bai, Zihao Zhou, Zhanhong Ou, Gregor Koehler, Raphael Stock, Klaus Maier-Hein, Marawan Elbatel, Robert Martí, Xiaomeng Li, Yaoyang Qiu, Panjie Gou, Gongping Chen, Lei Zhao, Jianxun Zhang, Yu Dai, Fangyijie Wang, Guénolé Silvestre, Kathleen Curran, Hongkun Sun, Jing Xu, Pengzhou Cai, Lu Jiang, Libin Lan, Dong Ni, Mei Zhong, Gaowen Chen, Víctor M. Campello, Yaosheng Lu, Karim Lekadir
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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)产生的结果往往不一致。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大会(MICCAI 2023)期间举办了耻骨联合-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采集的5101张产后超声图像。由于科学界的踊跃参与,初赛从 193 名参赛者的 179 个作品中选出了前 8 名进入第二阶段。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。最优秀的解决方案和完整的数据集将继续向公众开放,这将促进产前超声成像自动分割和生物测量的进一步发展。
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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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