Hi Gergely,

Thank you for your reply. I looked into the .flat file link you provided and installed the pyflat package. However, I could not figure out how to use pyflat.py to output a greyscale filetype with ABA greyscale values so I could use quantify fluorescence based on label intensity. I tried running the following line from the terminal within the directory containing visualign outputs:

python *\pyflat.py flat=4728114R_RGB_0000_nl.flat output=Grayscale.png

Even though the code ran without an error, no "Greyscale.png" output was produced. 

Ultimately, I envision a Numpy script where I can use the Visualigned atlas labels to mask each brain region and quantify fluorescence values, cell counts, etc in the original fluorescence image. The impedement to this is obtaining consistent greyscale visualigned atlas labels of high anatomical granularity to serve as the mask. I think I can achieve this by manually modifying all RGB values in the labels.txt file such that *_nl.png outputs have higher anatomical granularity and give appropriate and consistent grey values when converted to 16 bit images. Although, this approach requires me to manually change each of the 500+ RGB values in labels.txt file. Do you know of an easier way for me to accomplish this goal?

Visualign performs really well for registration, and I'm not aware of any other program that does so well (current apporach is Deepslice -> QuickNII -> Visualign -> custom FIJI macros to quantify fluorescence). Any advice would be greatly appreciated.

Kind regards,

Austen