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

22 Apr 2022, 22:36 (modified: 04 Jun 2022, 12:07)MIDL 2022 Short PapersReaders: Everyone
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.
Registration: I acknowledge that acceptance of this work at MIDL requires at least one of the authors to register and present the work during the conference.
Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Segmentation
Secondary Subject Area: Detection and Diagnosis
Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
1 Reply