Diastolic dysfunction is assessed by measurement of mitral annular (MA) early diastolic velocity (e’), commonly performed in echocardiography. Similar measurements can be obtained with valvular plane tracking in MRI long-axis cines. These measurements have been validated and have good reproducibility, yet manual MA points annotations are required. In this work we present a machine learning convolutional neural network with a residual architecture for automatic annotation of MA points in MRI long-axis cine images of the 2 and 4-chamber views. The landmark tracking allowed a fast and accurate evaluation of diastolic parameters improving the clinical applicability of MRI for diastolic assessment.