Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps

TL;DR

This paper introduces an interpretable atlas-learning segmentation method that automatically detects Hypo-plastic Left Heart Syndrome from fetal ultrasound images, achieving high diagnostic accuracy and clinical interpretability.

Contribution

It extends Atlas-ISTN to generate disease-specific atlases, enabling joint learning of segmentation, registration, atlas construction, and disease prediction.

Findings

Achieved an AUC-ROC of 0.978 in diagnosis

Comparable performance to expert manual diagnosis

Provides interpretable clinical insights

Abstract

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).