Igneous: Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling

William Silversmith, Aleksandar Zlateski, J. Alexander Bae, Ignacio Tartavull, Nico Kemnitz, Jingpeng Wu, H. Sebastian Seung

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Three-dimensional electron microscopy images of brain tissue and their dense segmentations are now petascale and growing. These volumes require the mass production of dense segmentation-derived neuron skeletons, multi-resolution meshes, image hierarchies (for both modalities) for visualization and analysis, and tools to manage the large amount of data. However, open tools for large-scale meshing, skeletonization, and data management have been missing. Igneous is a Python-based distributed computing framework that enables economical meshing, skeletonization, image hierarchy creation, and data management using cloud or cluster computing that has been proven to scale horizontally. We sketch Igneous's computing framework, show how to use it, and characterize its performance and data storage.

Original languageEnglish (US)
Article number977700
JournalFrontiers in neural circuits
StatePublished - Nov 25 2022

All Science Journal Classification (ASJC) codes

  • Sensory Systems
  • Cellular and Molecular Neuroscience
  • Cognitive Neuroscience
  • Neuroscience (miscellaneous)


  • cloud computing
  • compression
  • connectomics
  • distributed computing
  • image processing
  • meshing
  • neuroscience
  • skeletonization


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