The Connectomics at Google team and our academic collaborators have publicly released a segmentation of an entire fly brain for use in neuroscience and image processing research. We have also released an updated methods manuscript to accompany the dataset.

Released datasets and visualizations in Neuroglancer

The current released segmentation is FAFB-FFN1-20190805, freely viewable for exploration and circuit tracing.

Sampling of neurons reconstructed in the released segmentation:
FAFB neuron reconstructions animation

Description of Neuroglancer layers

  1. fafb_v14: the raw imagery of the publicly available Full Adult Fly Brain (FAFB) described by Zheng et al., 2018, in the latest v14 global alignment.
  2. fafb_v14_clahe: as above, with CLAHE post-processing.
  3. neuropil-regions-surface: 3d surface meshes for canonical fly brain regions of interest; details and label mapping described here.
  4. neuropil-full-surface: 3d surface mesh for the entire canonical fly brain also described at link above.
  5. fafb-ffn1-20190805: the current public automated segmentation, produced by Flood-Filling Networks (FFNs) with added procedures for handling common serial-section EM artifacts. See manuscript for details.
  6. skeletons_32nm: automated TEASAR skeletonization of the FAFB-FFN1-20190805 segmentation.
  7. public_skeletons: the publicly released manually traced skeletons from Zheng et al., 2018, in the latest v14 global alignment. Skeleton IDs from the valid ID list must be entered manually to select which IDs to display. See here for a simplified view with all public skeletons active.
  8. clefts_Heinrich_etal: synaptic cleft predictions from Heinrich et al., 2018.
The web viewer is Neuroglancer; see GitHub for documentation, or use the upper-right question mark button for quick tips.

Demo using Neuroglancer to explore Olfactory Projection Neurons and Kenyon Cells of the Mushroom Body in the previous 20190521 release:

Data access from Python via CloudVolume

Interactive access to the segmentation from Python is easy via CloudVolume. Basic access is demonstrated in this Colab notebook. Since looking up segment IDs at a series of points throughout the volume is a common use case, this notebook also demonstrates a method for batching point lookups to more efficiently access storage chunks.

Download links and format

Data can also be downloaded directly from Google Cloud Storage (e.g. via gsutil) from the links listed in Neuroglancer, e.g. for the segmentation: gs://fafb-ffn1-20190805/segmentation. The format specification for the data is described here.

64-bit TIFF image tiles of the segmentation at 16x16x40 nm resolution (1 image per section) can be downloaded at gs://fafb-ffn1-20190805/segmentation_tiffs/agglomerated_flat_16nm.

Manuscript

Changelog