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.
The current released segmentation is FAFB-FFN1-20190805, freely viewable for exploration and circuit tracing.
Sampling of neurons reconstructed in the released segmentation:
Demo using Neuroglancer to explore Olfactory Projection Neurons and Kenyon Cells of the Mushroom Body in the previous 20190521 release:
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.
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.
Reconstruction of neural circuitry at single-synapse resolution is an attractive target for improving understanding of the nervous system in health and disease. Serial section transmission electron microscopy (ssTEM) is among the most prolific imaging methods employed in pursuit of such reconstructions. We demonstrate how Flood-Filling Networks (FFNs) can be used to computationally segment a forty-teravoxel whole-brain Drosophila ssTEM volume. To compensate for data irregularities and imperfect global alignment, FFNs were combined with procedures that locally re-align serial sections as well as dynamically adjust and synthesize image content. The proposed approach produced a largely merger-free segmentation of the entire ssTEM Drosophila brain, which we make freely available. As compared to manual tracing using an efficient skeletonization strategy, the segmentation enabled circuit reconstruction and analysis workflows that were an order of magnitude faster.