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Lightweight Brain Tissue Segmentation

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.

Table of Contents

Overview

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.

Features

  • 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.

Installation

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

Usage

To segment brain tissue from your own MRI images:

  1. Place your MRI images in a directory.
  2. Run the following command:
python main.py --input_dir path/to/images --output_dir path/to/save/segmented_images

Training

To train the model on your own dataset:

  1. Prepare your dataset and update the configuration file (config.py).
  2. Start the training process:
python train.py

Evaluation

To evaluate the trained model on a test dataset:

python evaluate.py --test_data path/to/test_data