VoxelInsight is a conversational AI assistant for biomedical imaging that bridges large-scale data repositories and advanced image analysis tools — all through natural language. Built with Chainlit and OpenAI’s GPT-4o, VoxelInsight turns plain English into powerful radiology workflows.
- Search and explore datasets from platforms like MIDRC, IDC, and TCIA using plain English prompts
- Indexed metadata includes:
- Body part examined
- Imaging modality (CT, MR, PET, etc.)
- Study and series descriptions
- Scanner manufacturer and model
- Patient demographics and more
- Example queries:
- “Which collections contain liver CT data?”
- “Create a bar chart of patient counts per MIDRC collection”
- “How many MRI scanners were used in the UPenn GBM dataset?”
- Segmentation: Automatically segment organs, lesions, or tumors using TotalSegmentator
- Radiomics: Extract texture, shape, and first-order features using PyRadiomics
- Clinical Modeling: Train models to predict clinical endpoints
- Supports DICOM and NIfTI inputs
- Python 3.9 or higher
- pip (Python package installer)
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Clone the repository:
git clone https://github.com/BioIntelligence-Lab/VoxelInsight.git cd voxelinsight
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Install dependencies
pip install -r requirements.txt
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Setup Chainlit environment variables
Create a file named .env in the same folder as your app.py file. Add your OpenAI API key in the OPENAI_API_KEY variable.
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Run the Application
chainlit run app.py -w
Some example questions you can ask VoxelInsight:
- Which platforms contain COVID-19 data?
- List the collections on the MIDRC platform.
- How many patients are in the CheXpert dataset on AIMI?
- Segment the liver from this CT scan and give me its volume.
- Segment brain tumors from all patients in the upenn_gbm collection, extract radiomics, and train a MLP classifier to predict overall survival
VoxelInsight is continuously expanding its imaging intelligence. Upcoming features include:
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🧠 Expanded Model Library
Support for additional pretrained models on top of TotalSegmentator, including tumor and disease-specific segmentations. -
🧱 Foundation Model Integration
Plug-and-play with leading foundation models for medical imaging (e.g., BioMedCLIP, MERLIN) to enhance embedding-based retrieval and classification. -
🔄 Longitudinal Imaging Analysis
Track changes across timepoints using embeddings, volumes, and derived biomarkers to study treatment response or disease progression. -
📊 Quantitative Imaging Reports
Export structured reports summarizing volumetric, radiomic, and anatomical measurements from any imaging study. -
🧪 Interactive Visualization Tools
Scroll, overlay, and compare segmentations directly within the chat environment.
We welcome contributions including:
- 🧩 New segmentation or model integrations
- 🧪 Visualization and analysis tools
- 📚 Dataset plugins or indexing enhancements
- 🛠️ Documentation and usability improvements
How to contribute:
- Fork the repository
- Create a feature branch
- Submit a pull request with a clear description of your changes
For major features or ideas, please open an issue to start a discussion.
Let’s shape the future of imaging AI together!