Finding missed branches

This examples will illustrate how to use the Google segmentation to find potential missed branches.

We will take a single, manually traced neuron and try finding potentially missed branches by comparing it to the same Google skeleton.

First initialize connections to the manual and autoseg CATMAID instances set up - make sure to replace HTTP_USER, HTTP_PW and API_TOKEN with your corresponding credentials:

manual = pymaid.CatmaidInstance('https://neuropil.janelia.org/tracing/fafb/v14',
                                api_token='API_TOKEN',
                                http_user='HTTP_USER',
                                http_password='HTTP_PW',
                                caching=False,
                                max_threads=20)

auto = pymaid.CatmaidInstance('https://spine.itanna.io/catmaid/fafb-v14-seg-li-200412.0',
                              api_token='API_TOKEN',
                              http_user='HTTP_USER',
                              http_password='HTTP_PW',
                              caching=False,
                              max_threads=20)

Then fetch the neuron (exchange the 16 for the skeleton ID of your neuron):

x = pymaid.get_neuron(16, remote_instance=manual)

Now find and tag missed branches (see:func:fafbseg.google.find_missed_branches for additional parameters):

(summary,
 fragments,
 branches) = fafbseg.google.find_missed_branches(x, autoseg_instance=auto)

Show summary of missed branches:

summary.head()

Co-visualize your neuron and potentially overlapping autoseg fragments:

x.plot3d(color='w')
fragments.plot3d()

Visualize the potentially missed branches:

pymaid.clear3d()
x.plot3d(color='w')
branches.plot3d(color='r')