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Liver CT Segmentation Viewer

A web application for segmenting liver anomalies from CT scans using AI. Upload a CT scan to view segmentation results and preview the output.

Features

• Upload CT scans in .nii or .nii.gz format
• AI-based segmentation (UNet, PyTorch Lightning, TorchIO)
• Segmentation preview in browser
• Gradio interface for interactive exploration
• Modern UI with Tailwind CSS

Project Structure

app.py           # Flask backend (API endpoint for segmentation)
index.html       # Frontend UI
main.js          # Handles upload and preview logic
model.py         # AI model, training, and Gradio app

Setup Instructions

1. Python Environment

• Python 3.8+
• Recommended: Create a virtual environment

2. Install Dependencies

pip install flask flask-cors nibabel gradio matplotlib numpy celluloid torch torchio pytorch-lightning

3. Run the Backend

python app.py

• The Flask server will start at http://127.0.0.1:5000

4. Open the Frontend

• Open index.html in your browser
• Upload a CT scan and click "Segment & Preview"

5. Gradio Demo (Optional)

python model.py

• Launches an interactive Gradio app for segmentation

API

POST /predict

• Request: Multipart form with ct_scan file
• Response:
  • { "preview": "data:image/png;base64,..." } (if implemented)
  • { "message": "CT scan uploaded successfully. No AI processing performed." } (default)

Team

• Zayed
• Gaayathri Ganesh

License

MIT

Built With

• api
• code
• github
• google
• google-cloud-vertex-ai
• javascript
• javascript-**frameworks-&-libraries:**-tensorflow
• jupyter-notebook
• kaggle
• kaggle-(datasets)
• opencv-**cloud-&-platforms:**-google-cloud-(vertex-ai)
• python
• scikit-learn
• streamlit/react-(frontend-demo)-**databases:**-firebase-/-firestore-(user-data-&-logs)-**apis:**-google-vision-api-(optional-baseline-for-image-analysis)-**tools:**-jupyter-notebook
• tensorflow
• tensorflow.js
• vision
• vs