PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head

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.

Fig. 1

Workflow of the establishment of the proposed dataset. (a) 1358 images from 1124 pregnant women were collected. (b) The annotation team was made up of 2 physicians and 18 annotators. (c) For each ultrasound image, two annotators conducted initial annotation. These segmentations were merged and then adjusted by a physician to obtain the ground truth. (d) Based on the final ground truth of PSFH, AOP measurement consisted of ellipse fitting, line identification, and AOP calculation.