PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images

耻骨联合 胎头 超声波 分割 医学 三维超声 胎儿 产科 放射科 计算机科学 人工智能 计算机视觉 怀孕 骨盆 生物 遗传学
作者
Jieyun Bai,Zihao Zhou,Zhanhong Ou,Gregor Koehler,Raphael Stock,Klaus H. Maier‐Hein,Marawan Elbatel,Robert Martí,Xiaomeng Li,Yaoyang Qiu,Panjie Gou,Gongping Chen,Lingjun Zhao,Jie Cao,Yu Dai,Fangyijie Wang,G.C.M. Silvestre,Kathleen M. Curran,Hongkun Sun,Jinhua Xu,Pengzhou Cai,Jun Lu,Libin Lan,Dong Ni,Mei Zhong,Gaowen Chen,Víctor M. Campello,Yaosheng Lu,Karim Lekadir
出处
期刊:Medical Image Analysis [Elsevier]
卷期号:99: 103353-103353
标识
DOI:10.1016/j.media.2024.103353
摘要

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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