Autoproofreader is a django application which acts as a drop-in extension for CATMAID. It contains API endpoints and static files.

1. Install autoproofreader in whichever python environment is running CATMAID with pip install -e path/to/this/directory

2. Run python manage.py migrate to create the autoproofreader models.

3. Run python manage.py collectstatic -l to pick up autoproofreader's static files.

4. Add autoproofreader to the list of KNOWN_EXTENSIONS in django/projects/mysite/pipelinefiles.py.

The autoproofreader widget relies on a compute server to handle the computations involved with automatically proofreading a neuron reconstruction. Thus the first thing you have to do is set up a compute server.

When you add a new server through the admin page you will see the following fields:

1. Name: This is what will be displayed when listing available servers.

2. Address: The address used by ssh to reach this server.

3. Diluvian path: This field is depricated and will be removed, you can leave it empty.

4. Results directory: This is where all files for running jobs and gathering their results will be stored. After each job is done, this directory will be cleaned up so that storage does not become a problem.

5. Environment source path: It is recomended that you install the necessary packages sarbor in a virtual environment to avoid version requirement conflicts with other packages. The source path points to the activate script that creates your virtual environment. For example ~/.virtualenvs/autoproofreader/bin/activate.

6. Project whitelist: These are the projects from which this server is accessable. If you leave this field blank, all projects will have access to running jobs on this server.

7. ssh user: All jobs will be submitted through the back end under one user. This user will have to be added to the server and given permission to read and write from the Results directory, as well as have access to the environment_source_path.

8. ssh key: The ssh_user needs to be set up with a private/public key pair to securely access the server. You can read more about how to do this at https://www.ssh.com/ssh/keygen/. Once this is done, move the private key to django/projects/mysite/.ssh/. Whatever you name the private key file is what goes in this field. When connecting to the server the backend will look for a file called ssh key in django/projects/mysite/.ssh/.

1. Make sure there is a user called ssh user who has a public/private key pair configured for this server, with the private key stored in the appropriate location.
2. Create a virtual environment and pip install sarbor from https://github.com/pattonw/sarbor.
3. Make diluvian or the cached lsd segmentation sources available.

1. pip install diluvian into your virtual environment from https://github.com/pattonw/diluvian.

1. create a sensitives.json file. This file will be used to retrieve segmentations from a mongodb.

The sensitives.json file should look something like this:

{
    "frag_db_host": "0.0.0.0",
    "frag_db_name": "example_db_name",
    "edges_collection": "example_edge_collection",
    "fragments_file": "/absolute/path/to/fragments/zarr.zarr",
    "fragments_dataset": "/zarr/dataset/path",
}

Once you have a server set up to accept proofreading jobs, you can submit a job by openning up the autoproofreading widget in catmaid. In the Run tab, you can fill out the settings that you want.

The run settings section contains basic job information such as a name, which server to use, which skeleton to proofread, and what source the backend should use to obtain segmentations. If you choose a cached lsd job, you need only provide the path to the sensitives.json file for the mongodb you want to use. If you choose a diluvian job, you will also need to configure a diluvian model and an image volume, since it needs the raw data and the model weights to compute your segmentations, rather than just retrieving it from a database.

The sarbor settings section contains configuration for the sarbor package which does all of the computations for finding potential errors and ranking them. Here you can configure things specific to the skeleton, whether you want to downsample the skeleton and by how much, whether you want to smooth the skeleton. You can also specify segmentation specific fields such as the amount of downsampling you want to use and how large a field of view around each sample point you would like to use.

Once you are satisfied with the settings, you can click Download settings to store a copy of them to reuse later. Clicking upload settings with the file that you downloaded will then repopulate all the fields with the settings you have provided. Finally clicking Segment will start the job which you will be able to see in the Queued job table. Here you will see some basic information about your job while you wait for it to complete.

Once a job is complete you can see it in the completed jobs table. Here you can see the completed jobs and clicking on a name will select it for you. Once selected you can move to the rankings table to view your results. The two checkmarks at the top will toggle the proofread skeleton and the segmentations. clicking either a branch score or a connectivity score will move your field of view to the appropriate location and display the information you asked for.

If you clicked on a branch score and you have the proofread skeleton layer active, you should see a black and white virtual node pointing to the location of the expected missing branch. If you also display the segmentations you can gain some insight as to why that point was chosen. Generally it is a point that is confidently segmented, and most distant to all current sample points.

Clicking on the connectivity score will hide the whole proofread skeleton, except the edge between the two nodes that you are interested in.

After reviewing a node you can mark it as reviewed for future reference.