The global longitudinal strain of the myocardial tissue has been shown to be a better indicator of cardiac pathologies in the subclinical stage than other indices, such as the ejection fraction. This article presents a new deep learning approach for strain estimation in 2D echocardiograms. The proposed method improves the performance of the state of the art without losing stability with noisy echocardiograms and achieved an average end point error of 0.14 $\pm\ 0.17$ pixels in the estimation of the optical flow in the myocardium and an error of 1.34 $\pm\ 2.34$ % in the estimation of the global longitudinal strain indicator when evaluated in a synthetic echocardiographic dataset. Further research will validate the proposed method by a clinical in-vivo dataset. Clinical relevance- This paper presents a method to estimate the global longitudinal strain index in noisy echocardiograms, which promises to be a better indicator of cardiac pathologies in the subclinical stage than other indices such as the ejection fraction.