Hidden Markov Modeling for Maximum Likelihood Neuron Reconstruction

Manuscript

Athey, T.L., Tward, D.J., Mueller, U. et al. Hidden Markov modeling for maximum probability neuron reconstruction. Commun Biol 5, 388 (2022). https://doi.org/10.1038/s42003-022-03320-0.

Relevant directory

brainlit/experiemnts/ViterBrain

How to use ViterBrain

  • First, make sure that you have installed the brainlit package [Documentation].

  • Second, uncompress the data brainlit/experiments/ViterBrain/data/example.zip. brainlit/experiemnts/ViterBrain/data/sample.zip can also be used.

  • Make sure you are using Python3.9

  • Then, you can run some of the tutorial notebooks in the notebooks folder:

    • ViterBrain.ipynb - shows a programmatic example of the pipeline, based on zarr inputs.

    • fig3-voxels.ipynb - generates Figure 3 from the paper.

    • fig7-results.ipynb - generates Figure 7 from the paper.

    • other notebooks can be useful for referemce, they were used in generating results in the paper.

  • The files in the scripts folder also can be useful:
    • napari_gui.py - shows the GUI prototype.

      • click on colored fragment to select, red arrow will identify orientation.

      • o-key or switch states button to switch orientation of selected fragment.

      • click on another colored fragment (and hit o-key if necessary to switch orientation).

      • click on the labels layer in the left hand pane, then click somewhere on the image (not on a fragment)

      • t-key or trace button to trace between fragments.

      • c-key or clear selected states button to clear the selected fragments.

      • q-key or clear all button to clear all annotations.

      • n-key or next color button to change colors (3 total colors).

    • other scripts are for reference for benchmarking the timing of the pipeline.