med-yolo 1.0.0

Medical Image segmentation with YOLO

MED-YOLO: Medical Image Segmentation with YOLO

MED-YOLO is a powerful tool for medical image segmentation using YOLO (You Only Look Once) architecture. It provides an easy-to-use interface for segmenting various medical structures including organs, tumors, vessels, and bones.

Features

• Zero-shot medical image segmentation
• Support for multiple medical image formats (DICOM, NIfTI, TIFF)
• Pre-trained models for common medical structures
• Interactive web interface using Streamlit
• Batch processing capabilities
• Post-processing tools for medical image analysis

Installation

pip install med-yolo

Quick Start

from m_yolo import predict_yolo

# Load and process a medical image
results = predict_yolo.process_image(
    image_path="path/to/your/image.dcm",
    model_type="organ",  # or "tumor", "vessel", "bone"
    confidence=0.5
)

# Visualize results
results.show()

Web Interface

Run the interactive web interface:

med-yolo

This will start a Streamlit server where you can:

• Upload medical images
• Select segmentation models
• Adjust parameters
• View and export results

Supported Image Formats

• DICOM (.dcm)
• NIfTI (.nii, .nii.gz)
• TIFF (.tif, .tiff)
• PNG (.png)
• JPEG (.jpg, .jpeg)

Supported Segmentation Types

• Organs
• Tumors
• Blood Vessels
• Bones
• Other Medical Structures

Requirements

• Python >= 3.11
• CUDA-capable GPU (recommended)
• See setup.py for full list of dependencies

Citation

If you use MED-YOLO in your research, please cite:

@software{med_yolo2024,
  author = {Sumit Pandey},
  title = {MED-YOLO: Medical Image Segmentation with YOLO},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/sumit-ai-ml/MED-YOLO}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Contact

For questions and support, please open an issue on GitHub or contact:

• Author: Sumit Pandey
• Email: [e-mail]
• GitHub: sumit-ai-ml