Neuroglancer & CloudVolume#

This tutorial will show you how to access data from Neuroglancer using 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 need 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. For example:

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 on top of NAVis.
2. Google's flood-filling segmentation of an entire Drosophila brain.
3. The Allen Institute's MICrONs datasets. We have a separate tutorial on this!
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 cloud-volume is installed and up-to-date:

pip install cloud-volume -U

Once that's done we can start pulling data using cloud-volume. 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 using navis.patch_cloudvolume. That will teach cloudvolume to return NAVis neurons:

# This needs to be run only once at the beginning of each session
navis.patch_cloudvolume()

Now we can connect to our data source. Here we connect to the Google segmentation of the FAFB dataset:

# 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 some (mesh) neurons:

# Setting `as_navis=True` we will get us MeshNeurons
m = vol.mesh.get([4335355146, 2913913713, 2137190164, 2268989790], as_navis=True, lod=3)
m

navis.plot3d(
    m,
    legend_orientation="h"  # few neurons, so we can afford a horizontal legend
    )

# And one 2d plot for the tutorial thumbnail
import matplotlib.pyplot as plt
fig, ax = navis.plot2d(m[1], method='2d', view=("x", "-y"))
ax.set_axis_off()
ax.grid(False)
plt.tight_layout()

This also works for skeletons. Note though that not all datasets contain precomputed skeletons! For those cases you might want to check out navis.skeletonize to skeletonize neuron meshes.

sk = vol.skeleton.get([4335355146, 2913913713, 2137190164, 2268989790], as_navis=True)
sk

Total running time of the script: ( 0 minutes 4.111 seconds)

Download Python source code: plot_01_remote_cloudvolume.py

Download Jupyter notebook: plot_01_remote_cloudvolume.ipynb

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