Along with image quality being a very significant factor in segmentation, sampling resolution is also critical for correct tracking. If the movement is too great compared to the typical object separation, correct identification of movement can be challenging or even impossible.

If, however, we can increase the acquisition frequency so that the movement from one time-point to the next the confidence in the correct identification of tracks improves significantly.

As a rule of thumb, it is generally preferable to take images frequently enough that the typical movement of objects from one time-point to the next is no more than 20% of the typical distance between neighboring objects.

Similarly, in cases where we are looking to monitor intensity changes, it's important to take images frequently enough to measure those changes accurately. If those changes are rhythmical or affected by rhythms in the sample (e.g. heartbeats), the acquisition frequency for each time-point should be at least 4 times higher than the frequency of changes in the sample. 

All this taken together means that in many cases tracking will not be possible and looking at macro before/after changes may be the most suitable type of analysis. Also, in many cases, the sampling frequency needs, coupled with the exposure time needed to capture images of sufficient quality may restrict the acquisitions to single planes thereby limiting the ability to measure changes in 4D.