- This program is designed to detect and quantify the movements of cardiomyocytes in microscope videos. You need to have ImageJ and Fiji downloaded on your computer to use this program.
- Go to Fiji -> Plugins -> Install Plugin...
- Go to local Github repository and select proj_cardiomyocytes_movement -> new_algorithm -> get_track_mate_data.groovy. Press OK to popup windows. Do the same procedures to proj_cardiomyocytes_movement -> new_algorithm -> roi_xml_to_txt.py by repeating Install the plugins procedure.
- Go to the local Gihub repository (proj_cardiomyocytes_movement -> new_algorithm) then drag and drop the cell_movement_detector_V1.0.ijm to Fiji.
- Use Bio-Format to open your video by typing it in the search bar.
- Run the main script cell_movement_detector_V1.0.ijm (command + r in Mac). Select the directory you want to save the results.
- Set the threshold for detecting cells. Set the minimum number of slides the ROI has to last to be considered in the output.
- Check the satisfaction box if you want to continue or keep setting new threshold untill you are satisfied. Let the program do the work. Ignore any exception popup windows if the program is not aborted.
- Input the spike filtering parameters. spike nums specifies the expected number of spikes. spike persistance specifies the least number of slides persistance required to be considered as a spike. adjacent spikes gap specifies the least number of slides between two adjacent spikes. exact spike num is checked if the expected number of spikes is exactly the one you input; it is unchecked if the expected number of spikes is at least the one you input.
- Save the data as excel and the arrow animations by checking the boxes. Leave them blank if you just want to see a layer result.
- Set the number of layers to divide the ROIs. The layers are ordered based on the average movement lengths of ROIs in each layer.
- Finally, check the output result. The movement length is increasing from top to bottom. "num" is the number of ROIs in that layer.
Tinevez, JY.; Perry, N. & Schindelin, J. et al. (2016), "TrackMate: An open and extensible platform for single-particle tracking.", Methods 115: 80-90, PMID 27713081 (on Google Scholar).