Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
This addresses the challenge of limited resources and high skill requirements for detecting fetal heart malformations in clinical screening, though it is incremental as it extends an existing atlas-based framework.
The authors tackled the problem of automatically diagnosing Hypo-plastic Left Heart Syndrome (HLHS) from fetal ultrasound images by developing an interpretable atlas-learning segmentation method, achieving an AUC-ROC of 0.978 and diagnoses competitive with expert manual diagnosis.
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).