VolView Insight is an open-source platform that unifies medical imaging and clinical data into one extensible interface. It provides researchers with a realistic testbed for developing, testing, and extending multimodal models in settings that mirror clinical reality.

• 🔬 Unified access to imaging data (DICOM/DICOMWeb) and clinical records (FHIR/EHR).
• 🧠 Extensible backend for AI/ML pipelines, including deep learning inference.
• ⚡ Web-based UI built on VolView.
• 🔌 Pluggable integrations — Orthanc, HAPI on FHIR, and MedGemma are examples. Users can bring their own DICOM/FHIR servers or multimodal pipelines.

Install the following:

• Node.js (use nvm to manage versions)
• Python == 3.10.11
• Poetry 2.1.2
• Docker 28.0.04
• A DICOMWeb server (e.g., Orthanc)
• A FHIR server (e.g., HAPI on FHIR)

git clone https://github.com/KitwareMedical/volview-insight.git
cd volview-insight
git submodule update --init

# macOS only
cat ./core-volview-patches/MACOS_COMPATIBILITY.patch | git -C core/VolView apply

You can use any DICOMWeb server. Below is an example setup for running the Orthanc Docker container.

# Run Orthanc with DICOMWeb plugin and NO authentication (dev only)
docker run --rm -p 8042:8042 -p 4242:4242 \
  -e ORTHANC__AUTHENTICATION_ENABLED=false \
  -e DICOM_WEB_PLUGIN_ENABLED=true \
  orthancteam/orthanc

Verify at: http://localhost:8042/

You can use any FHIR R4 endpoint. Example with HAPI FHIR image:

docker pull smartonfhir/hapi-5:r4-empty
docker run -dp 3000:8080 smartonfhir/hapi-5:r4-empty

Verify at: http://localhost:3000/hapi-fhir-jpaserver/fhir/Patient

The Python backend executes multimodal pipelines. You can extend it with your own.

cd volview-insight/server
poetry env use /path/to/bin/python3.10
poetry install
poetry run python -m volview_server -P 4014 -H 0.0.0.0 volview_insight_methods.py

If you encounter issues, remove -P 4014.

• MedGemma: This is a gated public model from Hugging Face (requires account + access token and authentication via huggingface-cli login or environment variable HUGGINGFACE_HUB_TOKEN)
• A Lung Segmentation MONAI model: To use the lung segmentation model, the following checkpoint file needs to be installed in the volview-insight/server subfolder

cd volview-insight/server
curl https://data.kitware.com/api/v1/file/65bd8c2f03c3115909f73dd7/download --output segmentLungsModel-v1.0.ckpt

cd volview-insight/orthanc-proxy
nvm use 23.10.0
npm install
npm run dev

⚠️ Must use Node.js 23.10.0 for the proxy. Runs at port 5173.

Make sure you have access to a global python environment running Python 3.10.11. You will have problems with npm install --force using some later versions.

nvm use 18.17.1
npm install --force
npm run setup-project
npm run build
npm run preview

Now open: http://localhost:4173/

• DICOMWeb: Replace Orthanc with your own server.
• FHIR/EHR: Replace HAPI with your own server.
• Pipelines: Extend the Python backend with your custom multimodal methods.

We welcome contributions!

• Add new pipelines or models
• Add integrations with other EHR/DICOM backends
• Report bugs and propose features

This software is provided solely for research and educational purposes. It is a proof-of-concept research platform and is not intended for clinical use.

• This software has not been reviewed or approved by the U.S. Food and Drug Administration (FDA) or any other regulatory authority.
• It must not be used for diagnosis, treatment, or any clinical decision-making.
• No warranties or guarantees of performance, safety, or fitness for medical purposes are provided.

By using this software, you acknowledge that it is for non-clinical, investigational research only.

Apache 2.0 © Kitware