Downsampling, Meshing, and Skeletonizing with Igneous
Channel: William Silversmith
Date: 2022-11-28

This video shows how to use Igneous (https://github.com/seung-lab/igneous), Python software for processing Neuroglancer large 3D images.

We show how to take sample HDF5 electron microscopy images and segmentations from https://cremi.org/data/ and fully process them into multi-resolution, meshed, and skeletonized Neuroglancer Precomputed layers. This tutorial covers the simplest processing pathway and does not cover producing sharded versions of these data types, parameter selection, nor distributed processing. 

The tutorial consists of four parts: 
1. Creating a base Neuroglancer Precomputed image and installing Igneous. 
2. Creating multi-resolution image layers with Igneous (“downsampling”). 
3. Creating meshes.
4. Creating skeletons.

This video and above text are adapted from the Supplement to the following paper:

Silversmith, W., Zlateski, A., Bae, J.A., Tartavull, I., Kemnitz, N., Wu, J., Seung, H.S., 2022. Igneous: Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling. Frontiers in Neural Circuits 16.
https://www.frontiersin.org/articles/10.3389/fncir.2022.977700/full#supplementary-material

The completed project shown in the video can be downloaded here. Warning, 3GB!

https://storage.googleapis.com/seunglab/wms/igneous_demo.zip

All materials are offered under the Creative Commons CC-BY 4.0 License
https://creativecommons.org/licenses/by/4.0/

Introduction: (0:00)
Set Up: (1:52)
HDF5 to Precomputed: (3:08)
Downsampling EM: (7:35)
Downsampling Segmentation: (12:37)
Mesh Forging: (15:12)
Mesh Merging: (19:15)
Skeleton Forging: (20:39)
Skeleton Merging: (21:44)
Final Results: (22:21)