This might be more useful for anyone areas out of brightfield tissue samples and running subcellular detection on them. If you don't want to have an outer tissue annotation, the previous script will work fine, though. I went ahead and wrote a test case that works be picking all annotations that are not on the "outside/top" layer so that you can have your tissue annotation drawn (possibly in multiple parts) and then convert all annotations within the largest annotations into detections. Either way seems to work with subcellular detections. Is there a reason to eliminate the nucleus ROI? I do not see it show up in the hierarchy at least, though I suppose it does create duplicate measurements when adding area to the cell. Looks good, I get functional large pathCellObjects that I can run Subcellular spot detection in, which is amazing. I was mostly pointing it out for completeness. Your positive values seemed quite high compared to the background in that one image, so if the impact of the background is negligible. Finding a rolling adjusted background would be significantly harder, and I don't know if it is warranted in your situation. The adjusted sum, for example, would be something like (mean detection intensity - mean background intensity)*(detection area). It could also be easily handled in R, Python, or many other programs. So if you found an "average" background value for the whole slide, you could easily create a new measurement using getMeasurementList().putMeasurement() to create a "background adjusted mean." The actual math involved in calculating that value would depend on how you generated it and whether you were adjusting the sum or mean. You can use any cell values to create new values, and repeat as many times as you like, all within QuPath. It really depends on how you want to handle it. Keeping up with those updates is important! nd2) generated by the above method until I updated my version of the bioformats jar, which hadn't been done in about 6 months or more. It also turned out that I originally couldn't open the OME-TIFF (or the. nd2 file which only had data from 3 channels, but apparently that was enough. I think he ended up going with the original. Not sure how many images you want to do this for, but at least Right file names, and which channels to keep based on the file Macro script for that, as it will require quite a bit of picking out the You may end up wanting to ask on the ImageJ forums about writing a To stack), and finally create a composite (Image-Color-Merge channels). (Image-Stacks-Stack to images), edit the pixel size (Image-Properties),Ĭlose all of the blank channels, then remerge them (Image-Stack-Images ImageJ, as you have to import all 4 base images, split them all "Unfortunately, it will take quite a bit of work to set the images up in The odd metadata seems to be a result of generating the multiple image projection (from a z stack, not something I have any experience with) through the Nikon software, which I was able to fix using ImageJ, but it was a bit involved since I had to: Once bioformats was installed, he was able to open the. But I'm putting it here to get more people involved. Note: I cross posted this on Github and gave a great response. I've resorted to just using Photoshop and manually counting, but that's a huge pain.ĬellProfiler looks like it might have some cool features, and I presented the problem there too:īut could some combination of QuPath + Cell Profiler (or QuPath alone) get the job done? I feel like it can. I've tried to use Fiji and I still don't know how to do this in a straight forward way. This is such a core analysis for a lot of neuroscience. Optional for ISH: Count puncta inside of cells.Sometimes this will be binary (yes/no) but it'd be nice to make more bins like absent, low, medium and high. Count the overlapping events: I want to see which cells have combinations of fluorescent signals: red alone, red+green, green alone, etc.Identify the cellular staining: Usually, cells have (DAPI) and then one or more IHC signals (antibody) or ISH signal (RNAscope, which usually show as puncta).But counting and finding overlaps, that is the part that I need to automate. I'm willing to draw ROIs by hand or some combination of auto-detect plus clean-up by hand.Identify cells in spinal cord tissue sections, either automatically or by manually.QuPath looks like it could be an extremely useful tool for a standard analysis I do.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |