  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.
