TY - JOUR
T1 - Igneous
T2 - Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling
AU - Silversmith, William
AU - Zlateski, Aleksandar
AU - Bae, J. Alexander
AU - Tartavull, Ignacio
AU - Kemnitz, Nico
AU - Wu, Jingpeng
AU - Seung, H. Sebastian
N1 - Funding Information:
This research was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/ Interior Business Center (DoI/IBC) contract number D16PC0005, NIH/NIMH (U01MH114824, U01MH117072, and RF1MH117815), NIH/NINDS (U19NS104648 and R01NS104926), NIH/NEI (R01EY027036), and ARO (W911NF-12-1-0594). The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.
Funding Information:
The authors are pleased to acknowledge that the work reported on in this paper was substantially performed using the Princeton Research Computing resources at Princeton University which is consortium of groups led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology's Research Computing. Forrest Collman and Sven Dorkenwald helped test early versions of the skeletonization pipeline and contributed helpful discussions. Sergiy Popovych articulated the need for FileQueue and collaborated on testing it. Manuel Castro wrote the original version of DracoPy and added Draco mesh compression to Igneous. We graciously thank everyone that has previously (or will have in the future) contributed code to Igneous. We are grateful for assistance from Google, Amazon, and Intel.
Publisher Copyright:
Copyright © 2022 Silversmith, Zlateski, Bae, Tartavull, Kemnitz, Wu and Seung.
PY - 2022/11/25
Y1 - 2022/11/25
N2 - 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.
AB - 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.
KW - cloud computing
KW - compression
KW - connectomics
KW - distributed computing
KW - image processing
KW - meshing
KW - neuroscience
KW - skeletonization
UR - http://www.scopus.com/inward/record.url?scp=85143665624&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143665624&partnerID=8YFLogxK
U2 - 10.3389/fncir.2022.977700
DO - 10.3389/fncir.2022.977700
M3 - Article
C2 - 36506593
AN - SCOPUS:85143665624
SN - 1662-5110
VL - 16
JO - Frontiers in Neural Circuits
JF - Frontiers in Neural Circuits
M1 - 977700
ER -