Hidden Markov Modeling for Maximum Likelihood Neuron Reconstruction


Athey, T. L., Tward, D. J., Mueller, U., & Miller, M. I. (2021). Hidden Markov Modeling for Maximum Likelihood Neuron Reconstruction. arXiv preprint arXiv:2106.02701.

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.

  • 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 to switch orientation of selected fragment.

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

      • click no the labels layer in the left hand pane, then click somewhere on the image (not on a fragment)

      • t-key to trace between fragments.

      • c-key to clear the selected fragments.

      • q-key to clear all annotations.

      • n-key to change colors (3 total colors).

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