Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound

TL;DR

This paper introduces a novel unsupervised anomaly detection framework for fetal ultrasound images using diffusion models, significantly improving detection accuracy of anomalies in noisy, artifact-laden images.

Contribution

It pioneers the application of denoising diffusion probabilistic models for fetal ultrasound anomaly detection, enhancing diagnostic precision without supervision.

Findings

AutoDDPM achieved 79.8% AUPRC in anomaly detection.

The framework effectively handles noise and artifacts in ultrasound images.

Diffusion models outperform traditional methods in this context.

Abstract

Ultrasonography is an essential tool in mid-pregnancy for assessing fetal development, appreciated for its non-invasive and real-time imaging capabilities. Yet, the interpretation of ultrasound images is often complicated by acoustic shadows, speckle noise, and other artifacts that obscure crucial diagnostic details. To address these challenges, our study presents a novel unsupervised anomaly detection framework specifically designed for fetal ultrasound imaging. This framework incorporates gestational age filtering, precise identification of fetal standard planes, and targeted segmentation of brain regions to enhance diagnostic accuracy. Furthermore, we introduce the use of denoising diffusion probabilistic models in this context, marking a significant innovation in detecting previously unrecognized anomalies. We rigorously evaluated the framework using various diffusion-based anomaly detection methods, noise types, and noise levels. Notably, AutoDDPM emerged as the most effective, achieving an area under the precision-recall curve of 79.8\% in detecting anomalies. This advancement holds promise for improving the tools available for nuanced and effective prenatal diagnostics.