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How to run AxoID

First, enter the conda environment in which you have installed AxoID.

The main script can be launched as:

axoid /path/to/experiment [--option VALUE]

where /path/to/experiment point to an experiment folder, excluding the 2Pimg/ (e.g.: /data/lines/SS00001/2P/20190101/SS00001-tdTomGC6fopt-fly1/SS00001-tdTomGC6fopt-fly1-001/).

Similarly, the user correction GUI can be launched using:

axoid-gui /path/to/experiment [--option VALUE]

Note: if you run the GUI through ssh, you might need the option -X or -Y to forward the window to your screen. Additionally, note that it might be slower than running the GUI from your own machine.

Both scripts accept the argument --help for a list of their optional arguments.

Outputs

In this part, the folder with the experimental data given to AxoID is referred to as exp/, and should contain 2Pimg/, and potentially output/.

First, if they do not already exists, it will created images and image stacks in 2Pimg/:

  • warped_RGB.tif: image stack with the optic flow warped data
  • AVG_warped_RGB.tif: temporal average of warped_RGB.tif
  • AVG_warped_RGB.tif: temporal average of warped_RGB.tif tdTomato channel
  • ccreg_RGB.tif: image stack with the cross-correlation registered data
  • AVG_ccreg_RGB.tif: temporal average of ccreg_RGB.tif
  • AVG_ccreg_RGB.tif: temporal average of ccreg_RGB.tif tdTomato channel

Then, it will create 3 folders (if not already existing):

  • axoid_internal/: all the data internal to AxoID will be stored there, for debugging purposes, and for the GUI
  • GC6_auto/: folder storing the fluorescence traces, with the same structure as before
  • ROI_auto/: folder containing an image stack with the ROI contours overlayed over the raw data

In each of these folder, AxoID actually creates 3 subfolder in which it stores the same kind of data, but for different inputs:

  • raw/: contains the results and outputs of the AxoID pipeline applied to the raw data
  • ccreg/: contains the results and outputs of the AxoID pipeline applied to the cross-correlation registered data
  • warped/: contains the results and outputs of the AxoID pipeline applied to the optic flow warped data

In the axoid_internal folder, AxoID will saved the following outputs (note that raw/ might not have them all as its pipeline is slightly simplified, see Pipeline:

  • cuts.pkl: the cuts automatically applied to the model, as python pickled object
  • identities.tif: the final identity frames
  • identities_precut.tif: the identity frames before the automatic cuts were applied
  • indices:
  • indices_init.txt: text file with the indices of the frames used as similar frames for the projection and fine tuning. If no fine tuning (for raw/), it is the index of the frame used for initializing the tracker model
  • input.tif: image stack of the input data, be it the raw, cross-correlation registered, or warped data
  • input_fluo.tif: image stack used for extracting the fluorescence in the warped data (in opposite to input.tif which were used to find and track ROIs)
  • model.tif: image of the final tracker model with the different axons and their identities
  • model_precut: image of the tracker model with the different axons and their identities, before automatic cuts
  • rgb_init.tif: RGB image used to initialize the tracker model (either the temporal projection of the frame used for fine tuning, either a selected frame if no fine tuning)
  • seg_init.tif: binary segmentation of rgb_init.tif, used to initialize the tracker model
  • seg_init_cut.tif: binary segmentation of rgb_init.tif with cuts, used to automatically find cuts
  • segmentations.tif: stack of binary image corresponding to the ROI detections