Influence of Loss Function on Left Ventricular Volume and Ejection Fraction Estimation in Deep Neural Networks

Preshen Naidoo, Eman I Alajrami, Elisabeth Sarah Lane, Jevgeni Jevsikov, Matthew J Shun-shin, Darrel P Francis, Massoud Zolgharni

Keywords: Echocardiography, Left Ventricle Segmentation, Deep learning

TL;DR: Investigating the effects of established loss functions for left ventricular volume measurements from 2D echocardiographic images.

Abstract: Quantification of the left ventricle shape is crucial in evaluating cardiac function from 2D echocardiographic images. This study investigates the applicability of established loss functions when optimising the U-Net model for 2D echocardiographic left ventricular segmentation. Our results indicate loss functions are a significant component for optimal left ventricle volume measurements when established segmentation metrics could be imperceptible.

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Paper Type: novel methodological ideas without extensive validation

Primary Subject Area: Segmentation

Secondary Subject Area: Detection and Diagnosis

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