AbdulWahab Kabani and Mahmoud R. El-Sakka, "Ejection Fraction Estimation Using a Wide Convolutional Neural Network", International Conference on Image Analysis and Recognition, ICIAR'2017, LNCS 10317, pp. 87-96, Springer-Verlag Berlin Heidelberg, July 2017, Montreal, Canada.

Abstract

We introduce a method that can be used to estimate the ejection fraction and volume of the left ventricle. The method relies on a deep and wide convolutional neural network to localize the left ventricle from MRI images. Then, the systole and diastole images can be determined based on the size of the localized left ventricle. Next, the network is used in order to segment the region of interest from the diastole and systole images. The end systolic and diastolic volumes are computed and then used in order to compute the ejection fraction. By using a localization network before segmentation, we are able to achieve results that are on par with the state-of-the-art and by annotating only 25 training subjects (5% of the available training subjects).