This repository contains code for lightweight and efficient segmentation of brain tissues using deep learning models. The aim is to provide a streamlined approach for the automatic segmentation of brain tissue from MRI scans or similar medical images.
Brain tissue segmentation is a crucial step in medical image analysis, particularly for diagnosing and monitoring various neurological conditions. This repository provides a deep learning-based method for segmenting different types of brain tissues in an efficient and lightweight manner.
- Lightweight Model: A deep learning model optimized for quick and accurate brain tissue segmentation.
- Preprocessing Scripts: Tools to handle and prepare input data.
- Training Scripts: Easily train the model from scratch or fine-tune it on your data.
- Evaluation Scripts: Measure the model's performance on test data with minimal effort.
- Python Interface: User-friendly and easy to integrate into existing workflows.
Clone the repository and install the necessary dependencies:
git clone https://github.com/Paramahir/Lightweight-Brain-Tissue-Segmentation.git
cd Lightweight-Brain-Tissue-Segmentation
pip install -r requirements.txt
To segment brain tissue from your own MRI images:
- Place your MRI images in a directory.
- Run the following command:
python main.py --input_dir path/to/images --output_dir path/to/save/segmented_images
To train the model on your own dataset:
- Prepare your dataset and update the configuration file (
config.py
). - Start the training process:
python train.py
To evaluate the trained model on a test dataset:
python evaluate.py --test_data path/to/test_data