  Accurate LVEF measurement is important in clinical practice as it identifies
patients who may be in need of life-prolonging treatments. This paper presents
a deep learning based framework to automatically estimate left ventricular
ejection fraction from an entire 4-chamber apical echocardiogram video. The aim
of the proposed framework is to provide an interpretable and computationally
effective ejection fraction prediction pipeline. A lightweight Mobile U-Net
based network is developed to segment the left ventricle in each frame of an
echocardiogram video. An unsupervised LVEF estimation algorithm is implemented
based on Simpson's mono-plane method. Experimental results on a large public
dataset show that our proposed approach achieves comparable accuracy to the
state-of-the-art while being significantly more space and time efficient (with
5 times fewer parameters and 10 times fewer FLOPS).
