# 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.