Segmentation and analysis of OCT images

📖 Contents

• Introduction
• Data
• Methods
• Results
• Conclusion
• Requirements
• Installation
• How to Run
• Data Access
• How to Cite

🎯 Introduction - TO BE UPDATED SOON

📁 Data - TO BE UPDATED SOON

Source image	Pre-processed image

🔬 Methods - TO BE UPDATED SOON

📈 Results - TO BE UPDATED SOON

🏁 Conclusion - TO BE UPDATED SOON

💻 Requirements

• Operating System
  • macOS
  • Linux
  • Windows (limited testing carried out)
• Python 3.11.x
• Required core libraries: environment.yaml

⚙ Installation

Step 1: Install Miniconda

Installation guide: https://docs.conda.io/projects/miniconda/en/latest/index.html#quick-command-line-install

Step 2: Clone the repository and change the current working directory

git clone https://github.com/ViacheslavDanilov/oct_segmentation.git
cd oct_segmentation

Step 3: Set up an environment and install the necessary packages

chmod +x make_env.sh
./make_env.sh

🚀 How to Run - TO BE UPDATED SOON

Specify the data_path and save_dir parameters in the predict.yaml configuration file. By default, all images within the specified data_path will be processed and saved to the save_dir directory.

To run the pipeline, execute predict.py from your IDE or command prompt with:

python src/models/smp/predict.py

🔐 Data Access - TO BE UPDATED SOON

All essential components of the study, including the curated dataset and trained models, have been made publicly available:

• Dataset: https://zenodo.org
• Models: https://zenodo.org

🖊️ How to Cite - TO BE UPDATED SOON

Please cite OUR PAPER if you found our data, methods, or results helpful for your research:

Danilov V.V., Laptev V.V., Klyshnikov K.Yu., Ovcharenko E.A. (2024). PAPER TITLE. Journal Title. DOI: TO.BE.UPDATED.SOON