rusty_mws

A parallelized implementation of Rust-based Mutex Watershed image segmentation, built using MongoDB and Daisy for distributed processing.

Free software: Apache 2.0 License

Installation

Install Rust and Cargo via RustUp:

curl https://sh.rustup.rs -sSf | sh

Install MongoDB:

curl -fsSL https://pgp.mongodb.com/server-6.0.asc | sudo gpg -o /usr/share/keyrings/mongodb-server-6.0.gpg --dearmor

And initialize a MongoDB server in a screen on your machine:

screen
mongod

Install graph_tool

conda install -c conda-forge -c ostrokach-forge -c pkgw-forge graph-tool

Install rusty_mws:

pip install git+https://github.com/brianreicher/rusty_mws.git

Features

A mutex watershed implementation to go from affininities to a full segmentation
Able to run pipelines for skeleton-corrected segmentation (without graph agglomeration) or graph-based Mutex Watershed segmentation
Should handle up to 6 dimensional arrays of fragments (7 dimensions if you include offsets in affinities) but only tested on 2 and 3 dimensions

Usage

For seedless Mutex Watershed segmentation, using MongoDB:

import rusty_mws

pp: rusty_mws.PostProcessor = rusty_mws.PostProcessor(
            affs_file="../data/raw_predictions.zarr",
            affs_dataset="pred_affs_latest",
        )
pp.run_pred_segmentation_pipeline()

where:

affs_file is a path (relative or absolute) to the zarr file containing predicted affinities to generate fragments for.
affs_dataset is the name of the affinities dataset in the affs_file to read from.

For seeded skeleton-corrected segmentation with Mutex Watershed fragments:

import rusty_mws

pp: rusty_mws.PostProcessor = rusty_mws.PostProcessor(
            affs_file="../data/raw_predictions.zarr",
            affs_dataset="pred_affs_latest",
            seeds_file="../data/raw_predictions.zarr",
            seeds_dataset="training_gt_rasters",
        )
pp.run_corrected_segmentation_pipeline()