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

Authors :
Bai, Jieyun
Zhou, Zihao
Ou, Zhanhong
Koehler, Gregor
Stock, Raphael
Maier-Hein, Klaus
Elbatel, Marawan
Martí, Robert
Li, Xiaomeng
Qiu, Yaoyang
Gou, Panjie
Chen, Gongping
Zhao, Lei
Zhang, Jianxun
Dai, Yu
Wang, Fangyijie
Silvestre, Guénolé
Curran, Kathleen
Sun, Hongkun
Xu, Jing
Cai, Pengzhou
Jiang, Lu
Lan, Libin
Ni, Dong
Zhong, Mei
Chen, Gaowen
Campello, Víctor M.
Lu, Yaosheng
Lekadir, Karim
Publication Year :
2024

Abstract

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2409.10980
Document Type :
Working Paper