PSFHS
Bai, Jieyun

@article{zenodo_10969427,
  title= {PSFHS},
  journal= {},
  author= {Bai, Jieyun},
  year= {2024},
  url= {https://zenodo.org/records/10969427},
  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. The whole dataset used for the PSFHS challenge of MICCAI2023 (https://ps-fh-aop-2023.grand-challenge.org/) includes two parts: one is this PSFHS dataset and another is from the JNU-IFM dataset (https://doi.org/10.6084/m9.figshare.14371652). These images in the PSFHS dataset can also be used for the Intrapartum Ultrasound Grand Challenge (IUGC) 2024 of MICCAI 2024 (https://codalab.lisn.upsaclay.fr/competitions/18413).

**License:** https://creativecommons.org/licenses/by/4.0/. **Source:** https://zenodo.org/records/10969427

**Related:**
- references: 10.1002/uog.19072
- references: 10.1016/j.eswa.2023.123096
- references: 10.3389/fphys.2022.940150
- references: 10.1007/s11517-022-02747-1
- references: 10.1155/2022/5192338
- references: 10.1016/j.dib.2022.107904
- references: 10.5281/zenodo.7861699
- references: 10.6084/m9.figshare.14371652

**Related papers:**
- Chen, G., Bai, J., Ou, Z., Lu, Y., Wang, H. PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head. Scientific Data 11, 436 (2024). [doi:10.1038/s41597-024-03266-4](https://doi.org/10.1038/s41597-024-03266-4) · [Semantic Scholar](https://www.semanticscholar.org/paper/6b9cfa1346614a67915acd81742d7657740f2320)
- Bai, J., Zhou, Z., Ou, Z., et al. PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images. Medical Image Analysis (2024). arXiv:2409.10980 [doi:10.1016/j.media.2024.103353](https://doi.org/10.1016/j.media.2024.103353) · [Semantic Scholar](https://www.semanticscholar.org/paper/b2eeee065734599bdf4b3b776db0047e64b4e443)},
  files= {PSFHS.zip},
  keywords= {intrapartum ultrasound, PSFHS, Challenge, Angle of progression, head-symphysis distance, delivery mode, Segmentation, deep learning},
  license= {https://creativecommons.org/licenses/by/4.0/},
}