GOLDEN - Gold Labeling and Density Evaluation Network

Golden is an algorithm that identifies, clusters, and evaluates density of immunigold particles in electron microscopy images of the brain.

Golden uses a 'traditional algorithm' (non-machine learning) approach to identify the particles. It uses an approach derived from network theory to find the density of particles.

Python Version

Please use Python 3.12 or later with this project. Earlier versions are not guaranteed to work (3.11+ will probably work, but it may break in the future).

Dataset

Uses the immunogold dataset from Max Planck Florida Institute for Neuroscience. The paper is linked here.

Installation

To install the program, first clone the repository:

$ git clone https://github.com/AlexanderJCS/golden.git

Then, navigate to the directory and install the package:

$ cd golden
$ pip install -r ./requirements.txt

Installation is complete. To use the program, see the usage section.

Usage

View the help info by running the program with the -h flag:

# from the repository's root dir
$ python -m src.cli -h

This will show the following help info:

usage: Golden [-h] [-m] [-v] name

Find gold particles and their density in electron microscopy images

positional arguments:
  name          The name of the dataset to analyze. This is the name of the folder in the 'analyzed synapses' directory,e.g., 'S1' or 'S7'

options:
  -h, --help    show this help message and exit
  -m, --mask    Whether to apply the mask to the image before finding gold particles. Default: False
  -v, --visual  Whether to display the image with the gold particles marked on it. Default: False

To run the program, use the following command:

# from the repository's root dir
$ python -m src.cli S1

# S1 is the name of the image bundle to analyze, e.g., "S1", "S4", "S7" etc.
# See a full list by looking at the directory names in the "analyzed synapses" directory

You can also add the following flags:

Flag	Action
-m or --mask	Whether to apply the image mask found in the image bundle
-v or --visual	Whether to use matplotlib to show the results visually after calculation

Tests

To test the project, first navigate to the test package:

# from the repository's root dir
$ cd test

Then run the tests:

$ python -m pytest -k "not gold_accuracy_test"

We exclude the gold accuracy test since it is a long-runnning test and does not have a pass/fail condition. Instead, it is used to evaluate the accuracy of the gold-finding portion of the program by generating a confusion matrix. We can run it by using:

$ python -m pytest -k "gold_accuracy_test" -s

The -s flag is essential since it is used to print the confusion matrix to the console.

Contributing

Since this project is for a class, contributions are not open.