I am an undergraduate student working on designing a pipeline capable of measuring the skeleton of human fetal lung fibroblasts, which is something I have not really seen done in literature. I have been able to successfully use the measure object skeleton module to give me an accurate output, but I am confused as to how to interpret the results and/or manipulate them to give the measurements that I am looking for.
I start with composite images like the one below, with nuclei shown in white to be identified by IdentifyPrimaryObjects and cells in gray to be identified by IdentifySecondaryObjects.
plate1-dmso100k-1-comp.tif (1.2 MB)
Then, the images are run through ConvertObjectstoImage, Closing, Morphological Skeleton, and
MeasureImageSkeleton, and MeasureObjectSkeleton for a final result like this:
Although I think my pipeline is functioning well with recognizing cells, this skeleton that it is creating is not very helpful to what I am trying to accomplish, which is to measure each branch of the cells so that treatment groups can be compared in the number and length of branches. Is there some way to change my pipeline or use the data it gives me to accomplish this? I am also struggling with the fact that longer projections seem to be broken down into smaller segments, but it seems to me I have no way of identifying which segment is which in order to combine them, as the module does not give out the length of each branch, just the total skeleton per cell.
Also, would someone mind explaining to me what in my images would be counted as a Branch/Trunk/Endpoint? I have read the technical explanations involving the number of neighbors that each pixel has but I am struggling with relating it to my results. For example, would the number of endpoints be equal to the number of long branches protruding from the cell?