Fully automatic real-time ejection fraction and MAPSE measurements in 2D echocardiography using deep neural networks

Abstract

Cardiac ultrasound measurements such as left ventricular volume, ejection fraction (EF) and mitral annular plane systolic excursion (MAPSE) are time consuming and highly observer dependent. In this work, we investigate if deep neural networks can be used to fully automate cardiac ultrasound measurements in real-time while scanning. One neural network was used for identifying and separate the cardiac views while a second neural network performed segmentation of the left ventricle. By using TensorFlow, FAST and the highly optimized cuDNN backend real-time runtime of the entire pipeline was achieved with an average frames per second of 43, thus enabling these measurements to be performed while an operator is scanning. The measurement accuracy was evaluated using a Bland-Altmann analysis on a dataset of 75 patients resulting in (-13.7 ± 8.6)% for EF and (-0.9 ± 4.6) mm for MAPSE. It is concluded that deep learning can be used to fully automate these measurements, however more work remains to improve the accuracy.