EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography

TL;DR

EllipseNet is an innovative anchor-free neural network designed to automatically detect elliptical cardiac and thoracic regions in fetal echocardiograms, enabling efficient and consistent biometric measurements like CTR and cardiac axis.

Contribution

The paper introduces EllipseNet, a novel anchor-free ellipse detection network that accurately measures fetal cardiac biometrics from echocardiogram images, outperforming existing methods.

Findings

Outperforms several state-of-the-art methods

Evaluated on over 2000 clinical cases

Accurately measures CTR and cardiac axis

Abstract

As an important scan plane, four chamber view is routinely performed in both second trimester perinatal screening and fetal echocardiographic examinations. The biometrics in this plane including cardio-thoracic ratio (CTR) and cardiac axis are usually measured by sonographers for diagnosing congenital heart disease. However, due to the commonly existing artifacts like acoustic shadowing, the traditional manual measurements not only suffer from the low efficiency, but also with the inconsistent results depending on the operators' skills. In this paper, we present an anchor-free ellipse detection network, namely EllipseNet, which detects the cardiac and thoracic regions in ellipse and automatically calculates the CTR and cardiac axis for fetal cardiac biometrics in 4-chamber view. In particular, we formulate the network that detects the center of each object as points and regresses the ellipses' parameters simultaneously. We define an intersection-over-union loss to further regulate the regression procedure. We evaluate EllipseNet on clinical echocardiogram dataset with more than 2000 subjects. Experimental results show that the proposed framework outperforms several state-of-the-art methods. Source code will be available at https://git.openi.org.cn/capepoint/EllipseNet .