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BraTS 2021 Challenge - Segmentation Task

This repository is designed to evaluate state-of-the-art machine learning methods for brain tumor segmentation using multi-parametric MRI (mpMRI) scans. It provides a structured framework for data organization, model development, and evaluation, adhering to best practices in data science project management.

Project Organization

├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- Project documentation
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         brain_tumor_segmentation and configuration for tools like black
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.cfg          <- Configuration file for flake8
│
└── brain_tumor_segmentation   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes brain_tumor_segmentation a Python module
    │
    ├── config.py               <- Store useful variables and configuration
    │
    ├── dataset.py              <- Scripts to download or generate data
    │
    ├── features.py             <- Code to create features for modeling
    │
    ├── modeling                
    │   ├── __init__.py 
    │   ├── predict.py          <- Code to run model inference with trained models          
    │   └── train.py            <- Code to train models
    │
    └── plots.py                <- Code to create visualizations

TODO List

Task Status
Set up DVC for data versioning ❌ Not Started
Implement UNETR baseline Model ❌ Not Started
Implement V-Net baseline Model ❌ Not Started
Implement Swin-UNETR baseline Model ✅ Done
Optimize preprocessing pipeline ⏳ In Progress
Add unit tests for data processing scripts ❌ Not Started
Export trained model to ONNX format ❌ Not Started
Setup CI/CD pipeline for automated training & deployment ❌ Not Started
Deploy inference model via FastAPI or Flask ❌ Not Started

Getting Started

Clone the Repository

git clone https://github.com/cepa995/brats2021-challenge-segmentation-task.git

Set Up the Environment

Ensure you have Python 3.10 installed. Install the required dependencies:

pip install -r requirements.txt

Data Preparation

  1. Download the BraTS 2021 dataset from the official source.
  2. Place the data in the data/raw/ directory.
  3. Run preprocessing scripts as needed to prepare the data for modeling.

Training and Evaluation

Training

Utilize the train.py script to train your models. Ensure that your data is properly organized and preprocessed before initiating training.

###Evaluation After training, use the evaluate.py script to assess model performance on validation datasets.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your enhancements or bug fixes.