  Fetal ultrasound screening during pregnancy plays a vital role in the early
detection of fetal malformations which have potential long-term health impacts.
The level of skill required to diagnose such malformations from live ultrasound
during examination is high and resources for screening are often limited. We
present an interpretable, atlas-learning segmentation method for automatic
diagnosis of Hypo-plastic Left Heart Syndrome (HLHS) from a single `4 Chamber
Heart' view image. We propose to extend the recently introduced
Image-and-Spatial Transformer Networks (Atlas-ISTN) into a framework that
enables sensitising atlas generation to disease. In this framework we can
jointly learn image segmentation, registration, atlas construction and disease
prediction while providing a maximum level of clinical interpretability
compared to direct image classification methods. As a result our segmentation
allows diagnoses competitive with expert-derived manual diagnosis and yields an
AUC-ROC of 0.978 (1043 cases for training, 260 for validation and 325 for
testing).
