PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 13 of its 67 references that are in the map. First posted on arXiv 17 Sep 2024.
Builds on
13 of 67 references.
References · 13
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- 2016SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasoundcited by 71
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Citing papers per year: 2025: 4; 2026: 4.
8 papers.
- 2026Baseline Method of the Foundation Model Challenge for Ultrasound Image Analysiscited by 4
- 2025ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Qualitycited by 3
- 2025ERSR: An Ellipse-constrained pseudo-label refinement and symmetric regularization framework for semi-supervised fetal head segmentation in ultrasound imagescited by 2
- 2025Challenging DINOv3 Foundation Model under Low Inter-Class Variability: A Case Study on Fetal Brain Ultrasoundcited by 1
- 2026FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentationcited by 1
- 2026Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videoscited by 1
- 2025DAUNet: A Lightweight UNet Variant with Deformable Convolutions and Parameter-Free Attention for Medical Image Segmentationcited by 0
- 2026R2AoP: Reliable and Robust Angle of Progression Estimation from Intrapartum Ultrasoundcited by 0
- 2026R2AoP: Reliable and Robust Angle of Progression Estimation from Intrapartum Ultrasound20 May 2026
- 2026Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos13 Feb 2026
- 2026Baseline Method of the Foundation Model Challenge for Ultrasound Image Analysis1 Feb 2026
- 2026FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation22 Jan 2026
- 2025DAUNet: A Lightweight UNet Variant with Deformable Convolutions and Parameter-Free Attention for Medical Image Segmentation7 Dec 2025
- 2025Challenging DINOv3 Foundation Model under Low Inter-Class Variability: A Case Study on Fetal Brain Ultrasound1 Nov 2025
- 2025ERSR: An Ellipse-constrained pseudo-label refinement and symmetric regularization framework for semi-supervised fetal head segmentation in ultrasound images27 Aug 2025
- 2025ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality6 Mar 2025