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Analysis of HCR images using R, ImageJ and Ilastik
{\rtf1\ansi\ansicpg1252\cocoartf1561\cocoasubrtf600 {\fonttbl\f0\fswiss\fcharset0 ArialMT;} {\colortbl;\red255\green255\blue255;\red0\green0\blue0;} {\*\expandedcolortbl;;\cssrgb\c0\c0\c0;} \margl1440\margr1440\vieww13080\viewh14820\viewkind0 \deftab720 \pard\pardeftab720\sl280\partightenfactor0 \f0\b\fs24 \cf2 \expnd0\expndtw0\kerning0 \ul \ulc2 Protocol for unprocessed identification of Fcrls cells: \ \ Note: \ulnone Ensure image names match the format: DateOfExp_brain#_slice#_tile#_fcrls_ccl3.nd2 \b0 \ \fs36 0. Directory structure: \ a. Main directory with macros and code \ b. Subdirectories for each channel (eg. Ccl3/ and Fcrls/), each subdirectory has a TestData and a Segmentation directory \ c. RawData directory \ d. Output directory\ \ 1. In the raw data, using Fiji\'92s square selection and clear/clear outside commands, crop out regions outside the brain, hairs, over saturated pixels and other aberrations in the original file. \ 2. Save this as a tif, Prefixed with cropped (cropped_201903etc) in the raw data folder. \ 3. Split the channels in cropped files and save in the appropriate directories (eg. Ccl3/ and Fcrls/) in the TestData subdirectory \ 4. If not already done, create a ilp project and train a classifier on the TestData \ 5. Open eg. Fcrls_hemi.ilp and use the trained algorithm to identify cells in TestData. Run Ilastik batch mode to create simple segmentations and tifs. Save these in Segmentation directory. \ 6. Run Ilastik on all channel directories. \ 5. Run the \'93simple_segmentation_postprocess.ijm\'94 ImageJ macro on the segmentations of each channel. This will save postprocessed and DISPLAY images (with artificially dilated cells for easy viewing) in the Output directory. \ 6. Open R and run overlap_analysis.R which will call the the overlap_analysis.ijm macro and extract colocalized cells and numbers. May require some tweaking of code depending on the images. \ \ \ \ }
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