Kidney Cell Classification

EEL 4938/5840 Class Project

Project Overview

This project aims to develop an automated system for segmenting and classifying different cells within a histology kidney slice.

Group Members

• Joseph Cox
• Andrea McPherson
• Dylan Ogrodowsky
• Veronica Ramos

For any inquiries or further information, please contact cox.j@ufl.edu.

Installation

Prerequisites

Before installation, ensure you have the following:

• Python 3.12 or later
• pip package manager

In addition, we recommend having enough RAM to open both your CODEX file and your Stained Image file at the same time. While the classifier will still function without this, performance will be significantly impacted, and output results will be worse.

Dependencies

Setting Up a Virtual Environment

We recommend using a virtual environment to manage the dependencies for your project. This helps to avoid potential conflicts with other Python packages you may have installed. To set up a virtual environment, run the following commands in your terminal to create an environment called venv:

python -m venv venv

and then activating the virtual environment using

venv\Scripts\activate

on Windows or

source venv/bin/activate

on MacOS or Unix-like systems.

Installing Dependencies

Once your virtual environment is activated, install all necessary dependencies by running:

pip install -r requirements.text

which will install all packages listed in requirements.txt.

Preparing the Dataset

Ensure your dataset contains both a codex file and a Hematoxylin and Eosin (H&E) stained image, both in the tiff file format.

Running the Classifier

To start the classification process, run:

python classify.py --codex /path/to/codex.tiff --he /path/to/he_stained_image.tiff

Replace /path/to/codex.tiff and /path/to/he_stained_image.tiff with the actual path to your files. This script will segment, classify, and save the classification labels to a tiff file classified_stain.tif. Optionally, this output file can be set using the --output or -o flag in the classification script.

Contributing

We welcome contributions! Please read CONTRIBUTING.md for guidelines on how to submit contributions to this project.

License

This project is licensed under the GPLv3 License - see the LICENSE file for details.