Draft Status
Draft - team will hold off on page creation
Category
Infrastructure
Key Investigators
- Paul Dumont (University of North Carolina at Chapel Hill, USA)
- Alexandre Buisson (University of North Carolina at Chapel Hill, USA)
- Juan Carlos Prieto (University of North Carolina at Chapel Hill, USA)
- Lucia Cevidanes (University of North Carolina at Chapel Hill, USA)
- Steve Pieper (Isomics, USA)
Project Description
This project will attempt to build a free, confidential, and fully local alternative to cloud-based LLMs for medical imaging workflows. To bypass expensive and non-private cloud models, we will attempt to create an AI Agent for 3D Slicer designed to run entirely offline. We aim to enable private models powered by Ollama to accurately query the Slicer API without relying on external cloud infrastructure.
Objective
- Build a private MCP pipeline linking Ollama to 3D Slicer for secure, context-aware API scripting.
- Benchmark local model performance (Llama-3, DeepSeek) against cloud baselines for accuracy and cost.
Approach and Plan
Connect a local LLM client to existing Slicer MCP execution servers to enable code execution.
Evaluate zero-shot coding accuracy on multi-step Slicer workflows using purely local inference
Progress and Next Steps
Progress: Reviewed current cloud-reliant MCP integrations (slicer-skill, mcp-slicer) and local LLM baselines (SlicerChat).
Illustrations
No response
Background and References
slicerClaw: https://github.com/jumbojing/slicerClaw
mcp-slicer: https://github.com/zhaoyouj/mcp-slicer
slicer-skill: https://github.com/pieper/slicer-skill
SlicerChat: https://arxiv.org/abs/2407.11987
Draft Status
Draft - team will hold off on page creation
Category
Infrastructure
Key Investigators
Project Description
This project will attempt to build a free, confidential, and fully local alternative to cloud-based LLMs for medical imaging workflows. To bypass expensive and non-private cloud models, we will attempt to create an AI Agent for 3D Slicer designed to run entirely offline. We aim to enable private models powered by Ollama to accurately query the Slicer API without relying on external cloud infrastructure.
Objective
Approach and Plan
Connect a local LLM client to existing Slicer MCP execution servers to enable code execution.
Evaluate zero-shot coding accuracy on multi-step Slicer workflows using purely local inference
Progress and Next Steps
Progress: Reviewed current cloud-reliant MCP integrations (slicer-skill, mcp-slicer) and local LLM baselines (SlicerChat).
Illustrations
No response
Background and References
slicerClaw: https://github.com/jumbojing/slicerClaw
mcp-slicer: https://github.com/zhaoyouj/mcp-slicer
slicer-skill: https://github.com/pieper/slicer-skill
SlicerChat: https://arxiv.org/abs/2407.11987