Embedding the images like that requires me (or anyone else who wants to help) to download each one individually; a zip file would’ve really be more helpful. As it is your previous pipeline is close enough to right that I don’t think I can really improve it on my end; it’s something I can guide you in how to do generally but in the end you need to do it using your knowledge of the biology, the goals, and the whole movie or movies in question.
First, big picture- is there something different about those final frames that a) makes them biologically more interesting and important than the previous part of the movie b) makes them difficult to detect?
a) If you’re getting good tracing for the first 80%+ of the movie, that speaks well of your imaging technique and the overall accuracy of the tracking method. It’s worth considering how much time investment you want to put in improving this- again, it depends on details of your experiment and your own priorities. If it’s going to take you twice as long to set up the tracking for only an additional 20% increase in points tracked, is it worth it?
b) If you look at all of your frames as a whole, are the ones that track well vs poorly more or less crowded maybe? Are you having an issue because of bleaching of your fluorophore over time perhaps? That’ll give you an idea of what might be going wrong and how to fix it (ie if it’s bleaching, maybe you need to use different exposure conditions and/or include a Rescale module in your pipeline, if it’s because of crowding change your seeding conditions and/or spend some time perfecting the declumping settings in your IdentifyPrimaryObjects module).
As for the specific questions:
A) How can I fix the detection of all cells in the pipeline itself for all the frames?
Open up your pipeline in test mode and use “Choose image set” to navigate to a frame where it’s doing a poor job of segmenting and spend some time in the IdentifyPrimaryObjects module to see if you can improve it. Experiment with putting Rescale, Enhance, or Smooth modules in and segmenting on those instead of the raw. Try it for a few frames, then jump back to an earlier time point to make sure you haven’t wrecked the segmentation there- you may want to have a few different copies of your pipelines going or work in something like GitHub that has version control so that you make sure you can always get back to what you have now. It may take a few iterations, but hopefully you’ll find a more robust segmentation.
B) I know that I can optimize the parameters best for detection but still there would be some cell miss events in certain frames. For that I need to use LAP to track the cells and fill in the tracks. Could you suggest me how to do that in this pipeline?
Again, you’ll have to play with it a few frames at a time- if your cells don’t divide during the movie you may be able to set the split and merge costs to 1, and you can use the fact that you know you got good tracking with a radius of about 30 pixels in Distance mode so something comparable to that is probably good for your pixel radius in this mode too. Spend some time reading the help for that module, there are good tips for how to set the different costs and how to troubleshoot it.
Finally, like I mentioned in my previous post, if you need to track every single one of these cells across every frame and have every track editable after the fact, CP may not be the right tool for you. Anytime you’re going to completely automate the process and make it un-editable, as CP does, you sacrifice some amount of accuracy for speed of calculation and ease of scale up. We can minimize the inaccuracy with the steps above but it will never be 0, so if that’s unacceptable to your final needs you should use a tool that gives you the manual control you’re after.