Purpose The non-invasive examination of the cardiac functions is an important diagnostic, and follow up, tool for detection and evaluation of cardiovascular dysfunctions. Magnetic resonance (MR) cardiac tagging is a technique imposing temporary spatial variation in longitudinal magnetization around the heart area, so that temporary tag patterns deforming with cardiac motion can be captured in a sequence of MR images. The tag patterns play the role of noninvasive fiducial markers, by tracking of which an accurate estimation of cardiac motion can be achieved in vivo. This motion pattern can be subsequently analyzed and abnormalities can be detected and assessed. Segmentation of myocardium is an essential step for most of the approaches used to estimate dynamic displacement field since only the tag lines inside the myocardium are of importance for the deformation estimation. However, in most of the literature, segmentation of myocardium is done manually, which involves a large amount of tedious, timeconsuming and error-prone work. In the work presented here a fully automatic segmentation method is proposed for delineation of myocardial boundaries in the tagged MR images. Methods A fully-automatic method has been developed by the authors to segment myocardium of left ventricle by identifying epicardial and endocardial contours using implicit active contour models driven by tag structures and constrained by shape priors. The method has been developed to work, and tested with a temporal short-axis sequences of tagged cardiac MR images acquired using the SPAMM sequence. Results The initial assessment of the proposed method was performed against manual segmentation carried out by clinicians. The evaluation data consisted of five data sets each containing five or six tagged MR images. The results obtained for the six images from the third data set are shown in Fig. 2, where manual delineation and automatic delineation are represented by thick and thin lines respectively. It can be seen that in general there is relatively good correlation between manual and automatic delineation of the myocardial boundaries. It has to be noted that for the reported initial validation results no information is available about intra- or inter-observer variability and therefore it is impossible to quantitatively assess quality of the ground truth. Indeed a closer look at the images in Fig. 2 reveals that on some occasions (e.g. for images 2 and 5) the automatically selected contours are closer to the endocardial boundaries than the manual ones.