VoxelInsight

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 OpenAIs GPT-4o, VoxelInsight turns plain English into powerful radiology workflows.

Key Features

Natural Language Querying of Imaging Repositories

• 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?

AI-Powered Imaging Analysis

• 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

Installation & Setup

Requirements

• Python 3.9 or higher
• pip (Python package installer)

Step-by-Step Installation

1. Clone the repository:

git clone https://github.com/BioIntelligence-Lab/VoxelInsight.git cd voxelinsight

1. Install dependencies

   pip install -r requirements.txt

2. 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 OPENAIAPIKEY variable.

1. Run the Application

chainlit run app.py -w

Example Prompts

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

Roadmap & Upcoming Features

VoxelInsight is continuously expanding its imaging intelligence. Upcoming features include:

• 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.

Contributing

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: 1. Fork the repository 2. Create a feature branch 3. Submit a pull request with a clear description of your changes

For major features or ideas, please open an issue to start a discussion.
Lets shape the future of imaging AI together!