README.md

# Thyroid Ultrasound Segmentation Model

This repository contains a PyTorch-based U-Net model for segmenting thyroid nodules in ultrasound images. The model is designed to automatically identify and segment thyroid regions from ultrasound scans, providing both binary masks and confidence overlays.

## Model Architecture

The model uses a **U-Net architecture** with the following specifications:
- **Input**: Grayscale ultrasound images (224x224 pixels)
- **Output**: Binary segmentation masks and confidence overlays
- **Architecture**: U-Net with skip connections
- **Features**: [64, 128, 256, 512] channels in the encoder/decoder paths
- **Activation**: ReLU with batch normalization
- **Final Layer**: 1x1 convolution with sigmoid activation

## Model Details

- **Framework**: PyTorch
- **Input Channels**: 1 (grayscale)
- **Output Channels**: 1 (binary segmentation)
- **Input Size**: 224x224 pixels
- **Model Size**: ~31M parameters
- **Task**: Medical image segmentation (thyroid ultrasound)

## Usage

### Using the Model Directly

```python
import torch
from PIL import Image
import numpy as np

# Load the model
model = torch.load('best_model.pth', map_location='cpu')
model.eval()

# Preprocess image
def preprocess_image(image_path):
    image = Image.open(image_path).convert('L')
    image = image.resize((224, 224))
    image_array = np.array(image, dtype=np.float32) / 255.0
    image_tensor = torch.from_numpy(image_array).unsqueeze(0).unsqueeze(0)
    return image_tensor

# Run inference
input_tensor = preprocess_image('your_image.png')
with torch.no_grad():
    prediction = model(input_tensor)
    mask = torch.sigmoid(prediction) > 0.5
```

### Using the API

The model is also available as a FastAPI service with the following endpoints:

- `POST /segment-image/` - Upload and segment an image file
- `POST /segment-image-base64/` - Send base64 encoded image data
- `POST /segment-image-batch/` - Process multiple images in batch
- `GET /health` - Health check endpoint

## API Example

```python
import requests

# Segment an image file
with open('thyroid_ultrasound.png', 'rb') as f:
    files = {'file': f}
    response = requests.post('http://localhost:8000/segment-image/', files=files)
    result = response.json()

# Get segmentation mask and overlay
mask = result['segmentation_mask']
overlay = result['overlay_image']
confidence = result['confidence_score']
```

## Training

The model was trained on a dataset of thyroid ultrasound images with corresponding segmentation masks. The training process included:

- Data augmentation (rotation, scaling, flipping)
- Binary cross-entropy loss
- Adam optimizer
- Learning rate scheduling
- Early stopping based on validation performance

## Performance

- **Input Processing**: Supports common image formats (PNG, JPG, BMP, TIFF)
- **Output Quality**: High-resolution segmentation masks
- **Confidence Scoring**: Provides confidence overlays with color coding
- **Batch Processing**: Supports processing multiple images simultaneously

## Requirements

```
torch>=2.0.0
torchvision>=0.15.0
Pillow>=10.0.0
numpy>=1.24.0
fastapi>=0.104.1
uvicorn[standard]>=0.24.0
```

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{thyroid_segmentation_2024,
  title={Thyroid Ultrasound Segmentation using U-Net},
  author={Your Name},
  year={2024},
  url={https://huggingface.co/your-username/thyroid-segmentation}
}
```

## License

This model is released under the MIT License. See the LICENSE file for details.

## Contributing

Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests.

## Support

For questions or support, please open an issue on this repository or contact the maintainers.