Ultrasound imaging is one of the most prominent technologies to evaluate the growth, progression, and overall health of a fetus during its gestation. However, the interpretation of the data obtained from such studies is best left to expert physicians and technicians who are trained and well-versed in analyzing such images. To improve the clinical workflow and potentially develop an at-home ultrasound-based fetal monitoring platform, we present a novel fetus phantom ultrasound dataset, FPUS23, which can be used to identify (1) the correct diagnostic planes for estimating fetal biometric values, (2) fetus orientation, (3) their anatomical features, and (4) bounding boxes of the fetus phantom anatomies at 23 weeks gestation. The entire dataset is composed of 15,728 images, which are used to train four different Deep Neural Network models, built upon a ResNet34 backbone, for detecting aforementioned fetus features and use-cases. We have also evaluated the models trained using our FPUS23 dataset, to show that the information learned by these models can be used to substantially increase the accuracy on real-world ultrasound fetus datasets. We make the FPUS23 dataset and the pre-trained models publicly accessible at https://github.com/bharathprabakaran/FPUS23, which will further facilitate future research on fetal ultrasound imaging and analysis.

翻译：超声成像是评估胎儿在妊娠期间生长、发育和整体健康状况的最重要技术之一。然而，对此类研究获取的数据进行解读，最好由经过培训且精通此类图像分析的专业医师和技术人员完成。为了改善临床工作流程，并可能开发基于超声的家庭胎儿监护平台，我们提出了一个新的胎儿体模超声数据集FPUS23，该数据集可用于识别：（1）用于估算胎儿生物测量值的正确诊断标准切面；（2）胎儿方位；（3）解剖特征；（4）孕23周胎儿体模解剖结构的边界框。整个数据集包含15,728张图像，用于训练四个基于ResNet34骨干网络构建的不同深度神经网络模型，以检测上述胎儿特征及应用场景。我们还对使用FPUS23数据集训练的模型进行了评估，结果表明这些模型学习到的信息可用于显著提高在实际超声胎儿数据集上的准确性。我们将FPUS23数据集及预训练模型公开在https://github.com/bharathprabakaran/FPUS23，以进一步促进未来胎儿超声成像与分析的研究。