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In this study, we have tailored a pixel tracking method for temporalextrapolation of the ventricular segmentation masks in cardiac magneticresonance images. The pixel tracking process starts from the end-diastolicframe of the heart cycle using the available manually segmented images topredict the end-systolic segmentation mask. The superpixels approach is used todivide the raw images into smaller cells and in each time frame, new labels areassigned to the image cells which leads to tracking the movement of the heartwall elements through different frames. The tracked masks at the end of systoleare compared with the already available manually segmented masks and dicescores are found to be between 0.81 to 0.84. Considering the fact that theproposed method does not necessarily require a training dataset, it could be anattractive alternative approach to deep learning segmentation methods inscenarios where training data are limited.

Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking