https://github.com/viacheslavdanilov/oct_segmentation

This repository is dedicated to the segmentation of optical coherence tomography (OCT) images and the analysis of the plaques that appear on them
https://github.com/viacheslavdanilov/oct_segmentation

deep-learning image-processing machine-learning medical-imaging segmentation

Last synced: 8 days ago
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This repository is dedicated to the segmentation of optical coherence tomography (OCT) images and the analysis of the plaques that appear on them

• Host: GitHub
• URL: https://github.com/viacheslavdanilov/oct_segmentation
• Owner: ViacheslavDanilov
• License: mit
• Created: 2022-02-11T14:52:59.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2024-04-14T16:09:23.000Z (9 months ago)
• Last Synced: 2024-04-14T16:21:42.854Z (9 months ago)
• Topics: deep-learning, image-processing, machine-learning, medical-imaging, segmentation
• Language: Python
• Homepage: https://sites.google.com/view/viacheslav-danilov
• Size: 11.1 MB
• Stars: 2
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
[![DOI](http://img.shields.io/badge/DOI-TO.ADD.DATASET-blue)](https://TO.BE.UPDATED.SOON)

[![DOI](http://img.shields.io/badge/DOI-TO.ADD.MODELS-blue)](https://TO.BE.UPDATED.SOON)

[![DOI](http://img.shields.io/badge/DOI-TO.ADD.PAPER-B31B1B)](https://TO.BE.UPDATED.SOON)

# Segmentation and analysis of OCT images



## 📖 Contents

- [Introduction](#introduction)

- [Data](#data)

- [Methods](#methods)

- [Results](#results)

- [Conclusion](#conclusion)

- [Requirements](#requirements)

- [Installation](#installation)

- [How to Run](#how-to-run)

- [Data Access](#data-access)

- [How to Cite](#how-to-cite)



## 🎯 Introduction - TO BE UPDATED SOON



## 📁 Data - TO BE UPDATED SOON

|  ![Source image](.assets/source_img.png "Source image")  |  ![Pre-processed image](.assets/gray_img.png "Pre-processed image")  |

|:------------------------------------------------------:|:------------------------------------------------------------------:|

|                     *Source image*                     |                       *Pre-processed image*                        |



## 🔬 Methods - TO BE UPDATED SOON



## 📈 Results - TO BE UPDATED SOON



## 🏁 Conclusion - TO BE UPDATED SOON



## 💻 Requirements

- Operating System

  - [x] macOS

  - [x] Linux

  - [x] Windows (limited testing carried out)

- Python 3.11.x

- Required core libraries: [environment.yaml](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**

``` bash

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

cd oct_segmentation

```

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

``` bash

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](configs/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](src/models/smp/predict.py) from your IDE or command prompt with:

``` bash

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](https://zenodo.org)

- **Models:** [https://zenodo.org](https://zenodo.org)



## 🖊️ How to Cite - TO BE UPDATED SOON

Please cite [OUR PAPER](https://TO.BE.UPDATED.SOON) 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](TO.BE.UPDATED.SOON)