aind-segmentation-evaluation

Code Style

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 the predicted segmentation by comparing it to the ground truth skeleton. Once this comparison is complete, several statistics (e.g. edge accuracy, split count, merge count) are computed and returned in a dictionary. There is also an optional to write either tiff or swc files that highlight each topological mistake.

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

Metadata

Release files for aind-segmentation-evaluation 0.1.48

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for aind-segmentation-evaluation 0.1.48
File	Size	Uploaded
aind_segmentation_evaluation-0.1.48.tar.gz	119.1 kB	Jul 1, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for aind-segmentation-evaluation 0.1.48
File	Interpreter	ABI	Platform	Reset
aind_segmentation_evaluation-0.1.48-py3-none-any.whl 21.2 kB Jul 1, 2023	Python 3	none	any	Details

Total release size: 140.3 kB

Release files / aind_segmentation_evaluation-0.1.48.tar.gz

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