  In this study, we have tailored a pixel tracking method for temporal
extrapolation of the ventricular segmentation masks in cardiac magnetic
resonance images. The pixel tracking process starts from the end-diastolic
frame of the heart cycle using the available manually segmented images to
predict the end-systolic segmentation mask. The superpixels approach is used to
divide the raw images into smaller cells and in each time frame, new labels are
assigned to the image cells which leads to tracking the movement of the heart
wall elements through different frames. The tracked masks at the end of systole
are compared with the already available manually segmented masks and dice
scores are found to be between 0.81 to 0.84. Considering the fact that the
proposed method does not necessarily require a training dataset, it could be an
attractive alternative approach to deep learning segmentation methods in
scenarios where training data are limited.
