As you rightly guessed, the polygonal structures is likely due to the haematoxylin-stained nuclei having texture (these images are from mouse cerebellum):
That’s why I thought of using
Median Filter Radius to blur and smooth out the variation in pixel values within each nuclei, hopefully resulting in rounder nuclei. My experience with median filters so far has been that it is useful to smooth out pixel value variation while preserving edges. This did not help as I mentioned in the original post.
The base pixel size of the WSI I’m using is 0.2305 µm/pixel (40x). I have played with changing the
Estimated Pixel Size to use the original resolution (value = 0) and anywhere in between 0 to 2 µm, but it also did not help. I thought using the original resolution in combination with some median filtering would achieve the best smoothing while preserving the edges of nuclei.
My WSI are JPEG compressed by to ~60% quality (sadly, for storage reasons…) but I think with the textures I’m getting within the nuclei, no compression would exacerbate the issue. No compression would certainly improve nuclei edge detection though.
Trying to split very clumped objects like in the images above is definitely very tricky. Thank you for putting your thoughts down here in my post though, it did let me think about some potential solutions (that on further thinking, doesn’t work).
I will still try exporting clumped nuclei into ImageJ and play with the greyscale watershedding function there, as @petebankhead suggested. My workflow will look something like this:
- Cell detection in QuPath using relaxed sigma values.
- Identify detection of clumps by area larger than 1 average nucleus size.
- Export greyscale haematoxylin/OD sum images to ImageJ.
- Median filter to smooth pixel values and preserve edges.
- Threshold and create ROI around clump.
- Greyscale watershedding (fingers crossed).
- Send split ROI back to QuPath.
Seems like this will be deathly slow to process, but I’ll find out soon enough…