This project is an implementation of medical image segmentation and analysis using U-Net and its variants. It is specifically designed for segmenting and analyzing regions such as the liver and lungs in medical images.
- Utilizes U-Net architecture and its variants for accurate medical image segmentation.
- Provides segmentation and analysis capabilities for the liver, lungs, and other relevant regions.
- Supports preprocessing and postprocessing techniques to enhance segmentation results.
- Includes evaluation metrics to assess the accuracy and performance of the segmentation model.
- Offers visualization tools for displaying input images, predicted masks, and ground truth masks.
- Python 3.7 or higher
- PyTorch 1.6 or higher
- (TODO) NumPy, matplotlib, and other dependencies (see
requirements.txtfor full list)
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Clone the repository:
git clone ... -
Install the required dependencies:
pip install -r requirements.txt -
Prepare your dataset or use the provided sample dataset. (TODO)
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Run the training script to train the segmentation model:
python train.py
- Test module
- Requirements
- Intro
- Models module
- Visualization
This project is licensed under the MIT License.