Thanks so much for the fast response!
I’ll rephrase a bit with details about our current ImageJ pipeline: we take lightsheet microscopy images of cleared brain tissue in 2 or more channels, and save each “slice” in channel_slice.ome.tif format, (e.g. “C00_xyz_Table Z0000.ome.tif”), giving us >1000 individual images of each sample, scanned in as many channels as we need. We then want to combine all the individual slices in each channel into one stack, so we import all slices in a directory using Bio-Formats Importer (using the ability to divide by channel using the filename data I mentioned above). We then use Bio-Formats Exporter to re-export two new ome.tif files: one containing autofluorescence channel scans, and one with fluorophore scans. We can then work with this individual file to crop it for ClearMap and determine which parameters we should use for the program (more on ClearMap below). The time-consuming step is definitely the Bio-Formats export of the newly stacked ome.tif files, then subsequent modification (importing, cropping, etc.).
So yes, we’d like to increase the speed of reading/writing in new files in ImageJ, which might be beyond the scope of CLIJ, but thought you might also be able to suggest an alternative processing method? Hope that makes my question a little clearer.
ClearMap has similar functionality to the CLIJ2 processing example you provided, with functions to specify spot-detection, thresholding, etc., but it allows for alignment of signal throughout the stack to a brain atlas, automating the process of figuring out where our markers of interest are found throughout an entire brain. They’ve now released ClearMap2 for detection of/localization of markers near blood vessels as well: https://github.com/ChristophKirst/ClearMap2
Let me know if you need more information. Thanks again, I really really appreciate your time helping me speed up my group’s analysis pipeline!