AllenNeuralDynamics/segmentation-skeleton-metrics

A Python package for evaluating a predicted segmentation of neural arbors by using skeleton-based metrics. This tool detects topological mistakes (i.e. splits and merges) in a predicted segmentation by comparing it to a set of ground truth skeletons.

segmentation-skeleton-metrics

Python package for performing a skeleton-based evaluation of a predicted segmentation of neural arbors. This tool detects topological mistakes (i.e. splits and merges) in a predicted segmentation by comparing a set of ground truth skeletons to it. Once this comparison is complete, several statistics (e.g. edge accuracy, split count, merge count) are computed and returned in a dictionary.

Usage

Here is a simple example of evaluating a predicted segmentation. Note that this package supports a number of different input types, see documentation for details.

import os

from aind_segmentation_evaluation.evaluate import run_evaluation
from aind_segmentation_evaluation.conversions import volume_to_graph
from tifffile import imread


if __name__ == "__main__":

    # Initializations
    data_dir = "./resources"
    target_graphs_dir = os.path.join(data_dir, "target_graphs")
    path_to_target_labels = os.path.join(data_dir, "target_labels.tif")
    pred_labels = imread(os.path.join(data_dir, "pred_labels.tif"))
    pred_graphs = volume_to_graph(pred_labels)

    # Evaluation
    stats = run_evaluation(
        target_graphs_dir,
        path_to_target_labels,
        pred_graphs,
        pred_labels,
        filetype="tif",
        output="tif",
        output_dir=data_dir,
        permute=[2, 1, 0],
        scale=[1.101, 1.101, 1.101],
    )

    # Write out results
    print("Graph-based evaluation...")
    for key in stats.keys():
        print("   {}: {}".format(key, stats[key])

Installation

To use the software, in the root directory, run

pip install -e .

To develop the code, run

pip install -e .[dev]

To install this package from PyPI, run

pip install aind-segmentation-evaluation

Pull requests

For internal members, please create a branch. For external members, please fork the repository and open a pull request from the fork. We'll primarily use Angular style for commit messages. Roughly, they should follow the pattern:

<type>(<scope>): <short summary>

where scope (optional) describes the packages affected by the code changes and type (mandatory) is one of:

• build: Changes that affect build tools or external dependencies (example scopes: pyproject.toml, setup.py)
• ci: Changes to our CI configuration files and scripts (examples: .github/workflows/ci.yml)
• docs: Documentation only changes
• feat: A new feature
• fix: A bugfix
• perf: A code change that improves performance
• refactor: A code change that neither fixes a bug nor adds a feature
• test: Adding missing tests or correcting existing tests