Accurate fetal growth assessment from ultrasound (US) relies on precise biometry measured by manually identifying anatomical landmarks in standard planes. Manual landmarking is time-consuming, operator-dependent, and sensitive to variability across scanners and sites, limiting the reproducibility of automated approaches. There is a need for multi-source annotated datasets to develop artificial intelligence-assisted fetal growth assessment methods. To address this bottleneck, we present an open, multi-centre, multi-device benchmark dataset of fetal US images with expert anatomical landmark annotations for clinically used fetal biometric measurements. These measurements include head bi-parietal and occipito-frontal diameters, abdominal transverse and antero-posterior diameters, and femoral length. The dataset comprises 4,513 de-identified US images from 1,904 subjects acquired at three clinical sites using seven different US devices. We provide standardised, subject-disjoint train/test splits, evaluation code, and baseline results to enable fair and reproducible comparison of methods. Using an automatic biometry model, we quantify domain shift and demonstrate that training and evaluation confined to a single centre substantially overestimate performance relative to multi-centre testing. To the best of our knowledge, this is the first publicly available multi-centre, multi-device, landmark-annotated dataset that covers all primary fetal biometry measures, providing a robust benchmark for domain adaptation and multi-centre generalisation in fetal biometry and enabling more reliable AI-assisted fetal growth assessment across centres. All data, annotations, training code, and evaluation pipelines are made publicly available.

翻译：基于超声的胎儿生长准确评估依赖于在标准切面中通过手动识别解剖标志点来进行的精确生物测量。手动标志点标注耗时、依赖操作者，且对扫描设备和采集点之间的差异敏感，这限制了自动化方法的可重复性。需要多来源标注数据集来开发人工智能辅助的胎儿生长评估方法。为解决这一瓶颈，我们提出了一个开放的、多中心、多设备的胎儿超声图像基准数据集，该数据集包含用于临床胎儿生物测量的专家解剖标志点标注。这些测量包括头部双顶径和枕额径、腹部横径和前后径以及股骨长度。该数据集包含来自三个临床站点、使用七种不同超声设备采集的1,904名受试者的4,513张去标识化超声图像。我们提供了标准化的、受试者互斥的训练/测试划分、评估代码和基线结果，以实现方法的公平和可重复比较。通过使用一个自动生物测量模型，我们量化了域偏移，并证明局限于单一中心的训练和评估会显著高估相对于多中心测试的性能。据我们所知，这是首个公开可用的、覆盖所有主要胎儿生物测量指标的多中心、多设备、标志点标注数据集，为胎儿生物测量中的域适应和多中心泛化提供了一个稳健的基准，并使得跨中心的人工智能辅助胎儿生长评估更加可靠。所有数据、标注、训练代码和评估流程均已公开提供。