Ultrasound is the primary imaging modality in clinical practice during pregnancy. More than 140M fetuses are born yearly, resulting in numerous scans. The availability of a large volume of fetal ultrasound scans presents the opportunity to train robust machine learning models. However, the abundance of scans also has its challenges, as manual labeling of each image is needed for supervised methods. Labeling is typically labor-intensive and requires expertise to annotate the images accurately. This study presents an unsupervised approach for automatically clustering ultrasound images into a large range of fetal views, reducing or eliminating the need for manual labeling. Our Fetal Ultrasound Semantic Clustering (FUSC) method is developed using a large dataset of 88,063 images and further evaluated on an additional unseen dataset of 8,187 images achieving over 92% clustering purity. The result of our investigation hold the potential to significantly impact the field of fetal ultrasound imaging and pave the way for more advanced automated labeling solutions. Finally, we make the code and the experimental setup publicly available to help advance the field.

翻译：超声是妊娠期临床实践中的主要影像学检查手段。全球每年有超过1.4亿胎儿出生，导致大量超声检查的开展。大量胎儿超声图像的存在为训练鲁棒的机器学习模型提供了机遇。然而，海量超声图像也带来挑战：监督学习方法需要为每张图像进行人工标注。标注过程通常劳动密集且需要专业领域知识才能准确完成。本研究提出一种无监督方法，可自动将超声图像聚类至多种胎儿切面视图，从而减少或消除人工标注需求。我们基于88,063张图像的大规模数据集开发了胎儿超声语义聚类方法，并在另一组8,187张未接触图像的独立数据集上进行了评估，实现了超过92%的聚类纯度。本研究成果有望对胎儿超声成像领域产生重要影响，并为开发更先进的自动标注解决方案铺平道路。最后，我们公开了相关代码与实验配置，以推动该领域的发展。