Correct segmentation is crucial for successful tracking. An error of as little as 5% can mean that the mean correct track length would only be about 20 time-points. Of course, even incomplete tracks can still provide valuable information and some degree of error is inevitable, but generally, the better the segmentation, the better the tracking results. 

Some common problems and solutions in segmentation include:

  • Mean background or signal intensity changes over time - Use a normalization filter to even out the intensity range.
  • Noisy signal - Use denoising filters (a median filter can provide a good compromise between speed of processing and edge preservation).
  • Uneven background signal - The "Particle enhancement" denoising filter, or "Preserve bright objects" Morphology filter can both isolate bright features from their immediate background.

In many cases, the Blob Finder segmenter can over all of the above issues in one operation.

However, as stated above, the accuracy of the segmentation is particularly important for good tracking. Since tracking algorithms will typically try to find corresponding objects in timepoint n+1 for any objects in timepoint n, minimizing the number of incorrect or missing objects is particularly important. As such, there are typically two main problems with segmentation with regards to tracking:

  • Objects that are segmented but shouldn't, or vice versa
  • Objects that are incorrectly split, either splits that shouldn't exist or splits that didn't occur

Often the segmentation step will create objects that need not be tracked. In most cases, the objects to be tracked tend to have some common features that can be used to identify them from those that do not. We can use the Segment Feature Filter operation to tag only those objects we want:

Problems with splitting can usually be dealt with either by refining the segmentation method, either using different splitting parameters, or even a different segmentation operation, together with segment feature filters. the main aim is to avoid situations like the one here where, depending on the settings used, the tracking algorithm may need to decide what to do with two objects where there should only be one, or vice-versa.

Perfect segmentation is highly unlikely with non-perfect images, but we should strive to reduce the potential sources of error.