FUSC: Fetal Ultrasound Semantic Clustering of Second-Trimester Scans Using Deep Self-Supervised Learning

Abstract

Objective: The aim of this study was address the challenges posed by the manual labeling of fetal ultrasound images by introducing an unsupervised approach, the fetal ultrasound semantic clustering (FUSC) method. The primary objective was to automatically cluster a large volume of ultrasound images into various fetal views, reducing or eliminating the need for labor-intensive manual labeling.

Methods: The FUSC method was developed by using a substantial data set comprising 88,063 images. The methodology involves an unsupervised clustering approach to categorize ultrasound images into diverse fetal views. The method's effectiveness was further evaluated on an additional, unseen data set consisting of 8187 images. The evaluation included assessment of the clustering purity, and the entire process is detailed to provide insights into the method's performance.

Results: The FUSC method exhibited notable success, achieving >92% clustering purity on the evaluation data set of 8187 images. The results signify the feasibility of automatically clustering fetal ultrasound images without relying on manual labeling. The study showcases the potential of this approach in handling a large volume of ultrasound scans encountered in clinical practice, with implications for improving efficiency and accuracy in fetal ultrasound imaging.

Conclusion: The findings of this investigation suggest that the FUSC method holds significant promise for the field of fetal ultrasound imaging. By automating the clustering of ultrasound images, this approach has the potential to reduce the manual labeling burden, making the process more efficient. The results pave the way for advanced automated labeling solutions, contributing to the enhancement of clinical practices in fetal ultrasound imaging. Our code is available at https://github.com/BioMedIA-MBZUAI/FUSC.

Keywords: Deep clustering; Fetal ultrasound; Self-supervised learning.