Form factor

I am trying to use form factor to characterize some of my cells, but the data is the opposite of what I expected. I thought round objects should be closer to 1, and more spindle-like objects should be closer to zero. These very round cells


have a form factor of less than 0.1. Am I doing something wrong?
Thanks
-David
round cell tester pipeline.cp (6.8 KB)

Hi David,

Looking at the output of IdentifySecondaryObjects on the attached image, you can see that the objects being identified as cells are not even remotely round. You will need to further refine the settings in the module to more accurately capture the cell shape before you will see the expected results.

Regards,
-Mark


Sorry, I should’ve included this image. I cut down the size of the image so it could accurately identify the cells as shown.
round tester cell trace.pdf (86.2 KB)

By this last pdf, it looks like you want to measure the Form Factor of Primary objects (“Nuclei”) and not Secondary. If so, you can
(1) In MeasureObjectSizeShape, Add Another Object (or simply change from Cells) -> Nuclei
(2) Change DisplayHistogram to display Nuclei (first setting)
then you will get a histogram like the attached. This distribution is much higher. Though do note that the IDPrimary segmentation isn’t perfect and there are some long, skinny objects in the pdf, i.e. broken nuclear objects, that would necessarily have a low Form Factor.

Cheers,
David


Thanks for the reply, I am trying to measure the form factor of the cells. I understand that they will not be 1 or even near one, but what I found is that measuring images with many cells, my more epithelioid cell imagess were being measured as having lower form factors compared my stromal cell images. That’s why I thought this picture would be a good control, since these cells are pretty round. Any advice? What are you getting for the cell’s form factor?
Sorry to bother and thank you again,
David

Hi David,
The cells in the “round tester cell trace.pdf” are indeed round in the raw image. However the segmentations are not. Looking at the upper right panel of yours, “Labeled image”, e.g. neither the orange and yellow objects are round. They have large Perimeter-to-Area and thus low Form Factor = (4πArea/Perimeter^2). So I think the issue lies in better segmentation (the “Identify…” modules) and not the Form Factor calculation, per se.

I am attaching a pipeline. Some notes:

  • Adaptive thresholding methods are notorious for producing very “rough”, fractal-like patterns, as they propagate out from their seeds. This would increase the Perimeter / Area ratio a lot. I used a “Background Global” thresholding method in IdentifyPrimaryObjects instead.
  • The raw images have some “halos” around the perimeter of the objects (are these bright field images?). The segmentation might thus be improved (and thereby removing the spurious “cell” objects) if you first smooth the raw image a little. I added a Smooth module with a Gaussian filter (you can manually adjust the size if needed). This improves the segmentation of the Cells (to my eye) and also minimizes the Airy disk background artifact.
  • I added a DisplayDataOnImage so that you can see exactly which objects correspond to which Form Factors. I am getting Form Factors for the 3 cells in your cropped image of 0.35, 0.61, and 0.81. Note that one has a long tail, and another looks like it is actually two cells but one had no Hoechst staining so it did not have an IdentifyPrimaryObjects “seed”. So these lower-than-optimal values make sense. You would need to improve the Hoechst stain to get the latter to seed properly and the other with the long tail, well, just is!

Hope that helps.
Best,
David L.
round cell tester pipeline_DLogan.cp (7.58 KB)