Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome

TL;DR

This paper introduces an unsupervised learning framework using alpha-GAN to detect fetal heart anomalies, specifically Hypoplastic Left Heart Syndrome, from ultrasound images with improved accuracy and robustness.

Contribution

It presents a novel unsupervised approach that learns normal fetal heart anatomy exclusively from healthy cases, outperforming existing anomaly detection methods.

Findings

Achieved an average AUC of 0.81 in anomaly detection.

Demonstrated superior robustness to initialization compared to prior models.

Validated effectiveness specifically on Hypoplastic Left Heart Syndrome detection.

Abstract

Congenital heart disease is considered as one the most common groups of congenital malformations which affects $6-11$ per $1000$ newborns. In this work, an automated framework for detection of cardiac anomalies during ultrasound screening is proposed and evaluated on the example of Hypoplastic Left Heart Syndrome (HLHS), a sub-category of congenital heart disease. We propose an unsupervised approach that learns healthy anatomy exclusively from clinically confirmed normal control patients. We evaluate a number of known anomaly detection frameworks together with a model architecture based on the $\alpha$-GAN network and find evidence that the proposed model performs significantly better than the state-of-the-art in image-based anomaly detection, yielding average $0.81$ AUC \emph{and} a better robustness towards initialisation compared to previous works.