mlsanityData to 7 Oct 2026

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

arXiv 2409.10980 ↗First posted 17 Sep 2024eess.IV cs.CVcited by 8

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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

  1. 2023Segment Anythingcited by 8,158
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  3. 2023Segment Anything in Medical Imagescited by 226
  4. 2022Metrics reloaded: Recommendations for image analysis validationcited by 190
  5. 2023BiFormer: Vision Transformer with Bi-Level Routing Attentioncited by 150
  6. 2021DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentationcited by 126
  7. 2021Mixed Transformer U-Net For Medical Image Segmentationcited by 95
  8. 2016SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasoundcited by 71
  9. 2019Methods and open-source toolkit for analyzing and visualizing challenge resultscited by 60
  10. 2019BIAS: Transparent reporting of biomedical image analysis challengescited by 57
  11. 2022AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Imagescited by 43
  12. 2022A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysiscited by 29
1 more
  1. 2022Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-Viewcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 4.

8 papers.