Obstetrics and Gynecology Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China.
Sci Data. 2024 May 2;11(1):436. doi: 10.1038/s41597-024-03266-4.
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.
在分娩过程中，经会阴超声检查是一种非常有价值的工具，可以直接观察耻骨联合和胎头（PSFH）之间的相对位置关系。准确评估胎头下降和预测最合适的分娩方式都依赖于这种关系。然而，要对超声图像进行客观和定量的解释，需要进行精确的 PSFH 分割（PSFHS），这是一项既耗时又费力的任务。将人工智能（AI）的潜力应用于医学超声图像分割领域，基于 AI 的模型的开发和评估严重依赖于获取全面且精心标注的数据集。遗憾的是，专门针对 PSFHS 的公开数据集非常稀缺。为了弥补这一关键差距，我们引入了一个包含 1358 张图像的 PSFHS 数据集，这些图像都进行了像素级别的精细标注。标注过程遵循了标准化的协议，并涉及到医学专家的合作。值得注意的是，该数据集是迄今为止 PSFHS 领域中最广泛和最全面的资源。
