chunkflow 1.0.6

Composable image chunk operators to create pipeline for distributed computation.

chunkflow

Features

• Composable operators. The chunk operators could be composed in commandline for flexible usage.
• Hybrid Cloud Distributed computation in both local and cloud computers. The task scheduling frontend and computationally heavy backend are decoupled using AWS Simple Queue Service. The computational heavy backend could be any computer with internet connection and Amazon Web Services (AWS) authentication.
• Operators work with 3D image volumes.
• You can plugin your own code as an operator.

Image Segmentation Example

Perform Convolutional net inference to segment 3D image volume with one single command!

#!/bin/bash

chunkflow \
    read-tif --file-name path/of/image.tif -o image \
    inference --convnet-model path/of/model.py --convnet-weight-path path/of/weight.pt \
        --input-patch-size 20 256 256 --output-patch-overlap 4 64 64 --num-output-channels 3 \
        -f pytorch --batch-size 12 --mask-output-chunk -i image -o affs \
    agglomerate --threshold 0.7 --aff-threshold-low 0.001 --aff-threshold-high 0.9999 -i affs -o seg \
    neuroglancer -c image,affs,seg -p 33333 -v 30 6 6

you can see your 3D image and segmentation directly in Neuroglancer!

Operators

After installation, You can simply type chunkflow and it will list all the operators with help message. We keep adding new operators and will keep it update here. For the detailed usage, please checkout our Documentation.

Operator Name	Function
agglomerate	Watershed and agglomeration to segment affinity map
aggregate-skeleton-fragments	Merge skeleton fragments from chunks
channel-voting	Vote across channels of semantic map
cloud-watch	Realtime speedometer in AWS CloudWatch
connected-components	Threshold the boundary map to get a segmentation
copy-var	Copy a variable to a new name
create-chunk	Create a fake chunk for easy test
create-info	Create info file of Neuroglancer Precomputed volume
crop-margin	Crop the margin of a chunk
delete-chunk	Delete chunk in task to reduce RAM requirement
delete-task-in-queue	Delete the task in AWS SQS queue
downsample-upload	Downsample the chunk hierarchically and upload to volume
evaluate-segmentation	Compare segmentation chunks
fetch-task-from-file	Fetch task from a file
fetch-task-from-sqs	Fetch task from AWS SQS queue one by one
generate-tasks	Generate tasks one by one
inference	Convolutional net inference
log-summary	Summary of logs
mask	Black out the chunk based on another mask chunk
mask-out-objects	Mask out selected or small objects
mesh	Build 3D meshes from segmentation chunk
mesh-manifest	Collect mesh fragments for object
neuroglancer	Visualize chunks using neuroglancer
normalize-contrast-nkem	Normalize image contrast using histograms
normalize-intensity	Normalize image intensity to -1:1
normalize-section-shang	Normalization algorithm created by Shang
plugin	Import local code as a customized operator.
quantize	Quantize the affinity map
read-h5	Read HDF5 files
read-pngs	Read png files
read-precomputed	Cutout chunk from a local/cloud storage volume
read-tif	Read TIFF files
read-nrrd	Read NRRD files
remap-segmentation	Renumber a serials of segmentation chunks
setup-env	Prepare storage infor files and produce tasks
skeletonize	Create centerlines of objects in a segmentation chunk
threshold	Use a threshold to segment the probability map
view	Another chunk viewer in browser using CloudVolume
write-h5	Write chunk as HDF5 file
write-pngs	Save chunk as a serials of png files
write-precomputed	Save chunk to local/cloud storage volume
write-tif	Write chunk as TIFF file
write-nrrd	Write chunk as NRRD file

Reference

We have a paper for this repo:

@article{wu_chunkflow_2021,
	title = {Chunkflow: hybrid cloud processing of large {3D} images by convolutional nets},
	issn = {1548-7105},
	shorttitle = {Chunkflow},
	url = {https://www.nature.com/articles/s41592-021-01088-5},
	doi = {10.1038/s41592-021-01088-5},
	journal = {Nature Methods},
	author = {Wu, Jingpeng and Silversmith, William M. and Lee, Kisuk and Seung, H. Sebastian},
	year = {2021},
	pages = {1--2}
}