Estimating regional deformation of the myocardium from Cine MRI has the potential to locate abnormal tissue. Regional deformation of the left ventricle is commonly estimated using either segmentation or 3D+t registration. Segmentation is often performed at each instant separately from the others. It can be tedious and does not guarantee temporal causality. On the other hand, extracting regional parameters through image registration is highly dependent on the initial segmenta-tion chosen to propagate the deformation fields and may not be consistent with the myocardial contours. In this paper, we propose an intermediate approach that couples segmentation and registration in order to improve temporal causality while removing the influence of the chosen initial segmentation. We propose to apply the deformation fields from image registration (sparse Bayesian registration) to every segmentation of the cardiac cycle and combine them for more robust regional measurements. As an illustration, we describe local deformation through the measurement of AHA regional volumes. Maximum regional volume change is extracted and compared across scar and non-scar regions defined from delayed enhancement MRI on 20 ST-elevation myocardial infarction patients. The proposed approach shows (i) more robustness in extracting regional volumes than direct segmentation or standard registration and (ii) better performance in detecting scar.