PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
02 May 2024
02 May 2024
Historique:
received:
29
10
2023
accepted:
16
04
2024
medline:
3
5
2024
pubmed:
3
5
2024
entrez:
2
5
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 38698003
doi: 10.1038/s41597-024-03266-4
pii: 10.1038/s41597-024-03266-4
doi:
Types de publication
Dataset
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
436Subventions
Organisme : Natural Science Foundation of Guangdong Province (Guangdong Natural Science Foundation)
ID : 2023A1515012833
Organisme : Guangzhou Municipal Science and Technology Project
ID : 2024B03J1283
Organisme : Guangzhou Municipal Science and Technology Project
ID : 2024B03J1289
Informations de copyright
© 2024. The Author(s).
Références
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