Ejection fraction estimation using deep semantic segmentation neural network on 2D Echocardiography data

Abstract

Ejection fraction value denotes how much blood is pumped out of the heart to different parts of the body. It is a routine clinical procedure in heart function assessment, where the left ventricle of the heart has to be manually outlined by doctors in clinical settings to measure the EF value which is time consuming and highly varies by observer. Modern day deep learning methods are able to automatically complete this type of outlining task automatically with much ease and better efficiency even when the model is trained on a deeper neural network and smaller dataset. This paper investigates the deep semantic segmentation networks to find the most accurate one to implement an EF estimation system could be built on the most accurate image segmentation network which will reduce the pressure off the doctors shoulders and stop the eyeball estimation of EF values which is subject to inter-observer variability. This paper evaluated three different image segmentation neural networks namely U-Net, ResUNet, Deep ResUNet to find their accuracy score basing mostly on the dice accuracy metric. The most accurate model of the three Deep ResUNet has been utilized to form Left Ventricle segmentation network for end systole and end diastole images on which volume measurement formula is applied to find out the Ejection Fraction value.