Unsupervised detection of Hypoplastic Left Heart Syndrome in fetal screening

Congenital heart disease is considered as one the most common congenital malfor- mation which affects 6% − 11% per 1000 newborns. In this work, an automated framework for detection of cardiac anomalies during ultrasound screening examina- tions is proposed and evaluated on the example of Hypoplastic Left Heart Syndrome, 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 α-GAN network and find evidence that the proposed model shows a performance of 0.8 AUC and with a better robustness towards initialisation compared to individual state-of-the-art models.