PSFHS

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).产时经会阴超声检查是分娩过程中极具价值的工具，可直接观察耻骨联合与胎儿头部的相对位置关系（PSFH）。对胎儿头部下降程度的精准评估以及最佳分娩方式的预测，均高度依赖该位置关系。然而，要对超声图像进行客观且定量的解读，需要对PSFH进行精准分割（PSFHS），而该任务既耗时又对专业能力要求极高。 鉴于人工智能（AI）在医学超声图像分割领域的应用潜力，基于AI的模型开发与评估，高度依赖获取全面且经精细标注的数据集。遗憾的是，针对PSFHS任务的公开可用数据集极为匮乏。为填补这一关键空白，我们构建了一个包含1358张图像的PSFHS数据集，所有图像均经像素级精细标注。标注流程严格遵循标准化规范，并由医学专家协同完成。值得一提的是，该数据集是目前为止规模最大、内容最全面的PSFHS专用数据集。 用于MICCAI 2023的PSFHS挑战赛（https://ps-fh-aop-2023.grand-challenge.org/）的完整数据集包含两部分：其一为本数据集，其二为JNU-IFM数据集（https://doi.org/10.6084/m9.figshare.14371652）。本数据集的图像同时也可用于MICCAI 2024的产时超声大赛（Intrapartum Ultrasound Grand Challenge, IUGC）2024（https://codalab.lisn.upsaclay.fr/competitions/18413）。