Igneous: Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 25, 2022
- Source ID
- 10.3389/fncir.2022.977700
Entities
People
- Aleksandar Zlateski
- H. Sebastian Seung
- Ignacio Tartavull
- J. Alexander Bae
- Jingpeng Wu
- Nico Kemnitz
- William Silversmith
Organizations
- Army Research Office
- Intelligence Advanced Research Projects Activity
- National Eye Institute
- National Institute of Mental Health
- National Institute of Neurological Disorders and Stroke