autoseg

Automated segmentation for large-scale biological datasets.

• Free software: Apache 2.0 License

Installation

A complete install can be run with:

bash install.sh

Or install-by-install as follows:

1. Install Rust and Cargo via RustUp:

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

2. Install MongoDB:

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

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

screen
mongod

4. Install graph_tool

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

5. Install autoseg:

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

Features

TODO

Usage

This package is used for training, predicting, & evaluating deep learning segmentation models. The models are compatible with Zarr & N5 chunked image files, and volumes should be stored in the following format:

your_dataset.zarr/
|-- volumes/
|   |-- training_raw/
|   |   |-- 0/
|   |   |   |-- <raw_data_chunk_0>
|   |   |   |-- <raw_data_chunk_1>
|   |   |   |   ...
|   |   |-- 1/
|   |       ...
|-- training_labels/
|   |-- 0/
|   |   |-- <label_chunk_0>
|   |   |-- <label_chunk_1>
|   |   |   ...
|   |-- 1/
|       ...
|-- training_labels_masked/
|   |-- 0/
|   |   |-- <masked_label_chunk_0>
|   |   |-- <masked_label_chunk_1>
|   |   |   ...
|   |-- 1/
|       ...
|-- training_labels_unmasked/
    |-- 0/
    |   |-- <unmasked_label_chunk_0>
    |   |-- <unmasked_label_chun

1. The first step in the autoseg segmentation pipeline is predicting pixel affinities. Pointing the autoseg.train_model() driver function to a zarr with the following volumes will allow for training, along with hyperparameter tuning.

from autoseg import train_model


train_model(
    model_type="MTLSD",
    iterations=100000,
    warmup=100000,
    raw_file="path/to/your/raw/data.zarr",
    out_file="./raw_predictions.zarr",
    voxel_size=33,
    save_every=25000,
)

Functonality exists in autoseg.utils and autoseg.WebknossosToolkit() to handle data fetching, transformations, and conversions.

2. After affinities have been predicted, the autoseg.postprocess module is used to run Mutex Watershed or Merge Tree instance segmentation. Users can pass in the desired affinities Zarr to segment, as follows:

from autoseg import postprocess.get_validation_segmentation


get_validation_segmentation(
    segmentation_style: str = "mws",
    iteration="latest",
    raw_file="./data.zarr",
    raw_dataset="volumes/validation_raw",
    out_file="./validation.zarr",
)

TODO

Credits

This package builds upon segmentation pipelines & algorithms developed by Arlo Sheridan, Brian Reicher, Jeff Rhoades, Vijay Venu, and William Patton.