🦐 Trematode Scanner

AI-powered parasite detection tool for Petri dish analysis.

🎯 Problem

Biologists spend hours visually scanning Petri dishes with fish/sea urchin samples to detect trematode parasites (metacercariae). This is tedious, time-consuming, and leads to eye fatigue and missed detections.

💡 Solution

Trematode Scanner is a web application that uses computer vision (OpenCV + YOLOv8) to automatically detect suspicious circular objects in Petri dish images. It highlights potential parasites with green bounding boxes, significantly reducing manual screening time.

✨ Features

• 📸 Upload photos of Petri dishes (drag & drop support)
• 🧠 AI-powered detection of circular parasitic objects
• 🟩 Visual highlighting with bounding boxes
• 📊 Real-time detection count
• 🔄 Clean and intuitive user interface
• 🚀 FastAPI backend with async processing

🛠️ Tech Stack

Backend

• FastAPI — Modern Python web framework
• OpenCV — Image processing and contour detection
• Ultralytics YOLOv8 — Object detection (ready for custom training)
• Python 3.12

Frontend

• HTML5 / CSS3 — Clean, responsive interface
• Vanilla JavaScript — No frameworks needed
• REST API — Communication with backend

🚀 Quick Start

Prerequisites

• Python 3.12 or higher
• pip (Python package manager)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/trematode-scanner.git
cd trematode-scanner

2. Create and activate virtual environment:

cd backend
python -m venv venv

# On Windows:
venv\Scripts\activate
# On Mac/Linux:
source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Run the backend server:

uvicorn app:app --reload --host 0.0.0.0 --port 8000

5. Run the frontend (in a new terminal):

cd ../frontend
python -m http.server 5500

6. Open your browser at http://localhost:5500

🧪 How It Works

1. Upload: Biologist uploads a photo of a Petri dish
2. Detection: Backend processes the image using:
   • Adaptive thresholding to find dark regions
   • Contour detection to identify circular objects
   • Circularity filtering (0.7 < circularity < 1.3)
   • Size filtering (50 < area < 2000 pixels)
3. Visualization: Results are returned with green bounding boxes
4. Reporting: Detection count is displayed instantly

📊 Example

Input	Output

🔮 Future Improvements

• Train YOLOv8 on real trematode datasets
• Add SQLite database for analysis history
• Export results to CSV/Excel
• Support for microscope image formats (.tiff, .czi)
• Batch processing for multiple images
• User authentication and result sharing

🤝 Contributing

This is a proof-of-concept project. Contributions are welcome!

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

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

🙏 Acknowledgments

• Built for researchers working on trematode detection
• Inspired by real-world needs in parasitology labs

Project Link: https://github.com/bat801/trematode-scanner