Neuroglancer & CloudVolume

Neuroglancer is a WebGL-based viewer for volumetric data. You may have used it to browse some of the recent large EM datasets. If you want to programmatically access/download these data, you want CloudVolume. CloudVolume is an excellent Python library developed by William Silversmith (Seung lab, Princeton) and others. While CloudVolume is not directly related to Neuroglancer, it shares much of its functionality. As a rule of thumb: if you can view a dataset in Neuroglancer, you can download that data using CloudVolume. Some examples:

  1. FlyWire is a segmentation of an entire Drosophila brain. This dataset is very much work in progress and you will to register and apply for access. Check out FAFBseg for a fairly mature interface built using navis.

  2. Google’s flood-filling segmentation of an entire Drosophila brain.

  3. The Allen Institute’s MICrONs datasets.

  4. The Janelia hemibrain connectome.

CloudVolume supports the backends/data formats of these and many up-and-coming datasets. You can use it to query the segmentation directly, and to fetch meshes and skeletons (if available). navis & friends provide simple interfaces for some of the datasets (see e.g. the neuPrint and the MICrONs tutorials) but there is also some lower-level option to pull neurons into navis via CloudVolume.

First of all, you will want to make sure to cloudvolume is installed and up-to-date:

pip3 install cloud-volume -U

Once that’s done we can start pulling data using cloudvolume. In this example here, we will use the Google segmentation of the FAFB dataset:

import navis
import cloudvolume as cv

Before we connect to the database we have to “monkey patch” cloudvolume such that it returns navis neurons:

# This needs to be run only once at the beginning of each session
navis.patch_cloudvolume()
INFO  : cloud-volume successfully patched! (navis)

Now we can connect to our data source:

# Don't forget to set `use_https=True` to avoid having to setup Google credentials
vol = cv.CloudVolume('precomputed://gs://fafb-ffn1-20200412/segmentation', use_https=True, progress=False)

Fetch a some (mesh) neurons:

# If we set `as_navis=True` we will get MeshNeurons
m = vol.mesh.get([4335355146, 2913913713, 2137190164, 2268989790], as_navis=True, lod=3)
m
<class 'navis.core.neuronlist.NeuronList'> containing 4 neurons (3.4MiB)
type name id units n_vertices n_faces
0 navis.MeshNeuron None 4335355146 1 nanometer 14784 30420
1 navis.MeshNeuron None 2913913713 1 nanometer 15388 31213
2 navis.MeshNeuron None 2137190164 1 nanometer 8631 17612
3 navis.MeshNeuron None 2268989790 1 nanometer 9450 19189
fig = m.plot3d()