ArticleScientific data2024
PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Review
- A universal foundation model for grounded biomedical image interpretation.Nature communications · 2026Article
- Maternal-Fetal Ultrasouno Video Dataset for End-to-end Intrapartum Biometry and Multi-task Learning.Scientific data · 2026Article
- Review
- Analysis of intra- and inter-observer variability in 4D liver ultrasound landmark labeling.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Advancing prenatal healthcare by explainable AI enhanced fetal ultrasound image segmentation using U-Net++ with attention mechanisms.Scientific reports · 2025Article
- An annotated heterogeneous ultrasound database.Scientific data · 2025Article
- PSFHSP-Net: an efficient lightweight network for identifying pubic symphysis-fetal head standard plane from intrapartum ultrasound images.Medical & biological engineering & computing · 2024Article
- Editorial: New technologies improve maternal and newborn safety.Frontiers in medical technology · 2024Article
- A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
During the process of labor, the intrapartum transperineal ultrasound examination serves as a valuable tool, allowing direct observation of the relative positional relationship between the pubic symphysis and fetal head (PSFH). Accurate assessment of fetal head descent and the prediction of the most suitable mode of delivery heavily rely on this relationship. However, achieving an objective and quantitative interpretation of the ultrasound images necessitates precise PSFH segmentation (PSFHS), a task that is both time-consuming and demanding. Integrating the potential of artificial intelligence (AI) in the field of medical ultrasound image segmentation, the development and evaluation of AI-based models rely significantly on access to comprehensive and meticulously annotated datasets. Unfortunately, publicly accessible datasets tailored for PSFHS are notably scarce. Bridging this critical gap, we introduce a PSFHS dataset comprising 1358 images, meticulously annotated at the pixel level. The annotation process adhered to standardized protocols and involved collaboration among medical experts. Remarkably, this dataset stands as the most expansive and comprehensive resource for PSFHS to date.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.