Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening

Jeremy Tan; Anselm Au; Qingjie Meng; Sandy FinesilverSmith; John Simpson; Daniel Rueckert; Reza Razavi; Thomas Day; David Lloyd; Bernhard Kainz

arXiv · 2008.06966

Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening

Jeremy Tan·Anselm Au·Qingjie Meng·Sandy FinesilverSmith·John Simpson·Daniel Rueckert·Reza Razavi·Thomas Day·David Lloyd·Bernhard Kainz

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Abstract

Prenatal screening with ultrasound can lower neonatal mortality significantly for selected cardiac abnormalities. However, the need for human expertise, coupled with the high volume of screening cases, limits the practically achievable detection rates. In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound. We propose a pipeline for automated data curation and classification. During both training and inference, we exploit an auxiliary view classification task to bias features toward relevant cardiac structures. This bias helps to improve in F1-scores from 0.72 and 0.77 to 0.87 and 0.85 for healthy and CHD classes respectively.

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Jeremy Tan, Anselm Au, Qingjie Meng, Sandy FinesilverSmith, John Simpson, Daniel Rueckert, Reza Razavi, Thomas Day, David Lloyd, Bernhard Kainz. 2020-08-16. Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening. https://arxiv.org/abs/2008.06966

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