PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
<|reference_start|>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.<|reference_end|>
@article{bai2024psfhs,
    title={PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from
  Intrapartum Ultrasound Images},
    author={Jieyun Bai, Zihao Zhou, Zhanhong Ou, Gregor Koehler, Raphael Stock,
  Klaus Maier-Hein, Marawan Elbatel, Robert Mart'i, Xiaomeng Li, Yaoyang Qiu,
  Panjie Gou, Gongping Chen, Lei Zhao, Jianxun Zhang, Yu Dai, Fangyijie Wang,
  Gu'enol'e Silvestre, Kathleen Curran, Hongkun Sun, Jing Xu, Pengzhou Cai,
  Lu Jiang, Libin Lan, Dong Ni, Mei Zhong, Gaowen Chen, V'ictor M. Campello,
  Yaosheng Lu, Karim Lekadir},
    journal={arXiv preprint arXiv:2409.10980},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10980},
    primaryClass={eess.IV cs.CV}
}