How do I use Flow-Registration MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Flow-Registration MCP apply motion correction to the 2P microscopy video 'sample_scan.tif'" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

🚧 Under Development

This project is still in an alpha stage. Implementation is not complete. Expect rapid changes and incomplete features.

Flow-Registration MCP

Model Context Protocol (MCP) server for Flow-Registration - variational optical-flow motion correction for 2-photon (2P) microscopy videos and volumetric 3D scans.

This MCP server provides programmatic access to Flow-Registration functionality through the Model Context Protocol, enabling AI assistants and other MCP clients to perform motion correction on microscopy data.

Related projects

Original Flow-Registration repo: https://github.com/FlowRegSuite/flow_registration
Python implementation: https://github.com/FlowRegSuite/pyflowreg
ImageJ/Fiji plugin: https://github.com/FlowRegSuite/flow_registration_IJ
Napari plugin: https://github.com/FlowRegSuite/napari-flowreg

Fig1

Requirements

Python 3.10 or higher
FastMCP framework

Installation

Clone the repository and install dependencies:

git clone https://github.com/FlowRegSuite/flowreg-mcp.git cd flowreg-mcp pip install -r requirements.txt

Setup as MCP Server

To use this as an MCP server with Claude Desktop or other MCP clients:

Install the MCP server:

pip install -e .

Configure your MCP client (e.g., Claude Desktop) to connect to the server:

{ "mcpServers": { "flowreg": { "command": "python", "args": ["-m", "flowreg_mcp"], "cwd": "/path/to/flowreg-mcp" } } }

Getting started

Once configured, the MCP server exposes Flow-Registration functionality through standard MCP tools and resources. The server provides motion correction capabilities for microscopy data through a programmatic interface.

Dataset

The dataset which we used for our evaluations is available as 2-Photon Movies with Motion Artifacts.

Citation

Details on the original method and video results can be found here.

If you use parts of this code or the plugin for your work, please cite

“Pyflowreg,” (in preparation), 2025.

and for Flow-Registration

P. Flotho, S. Nomura, B. Kuhn and D. J. Strauss, “Software for Non-Parametric Image Registration of 2-Photon Imaging Data,” J Biophotonics, 2022. doi:https://doi.org/10.1002/jbio.202100330

BibTeX entry

@article{flotea2022a, author = {Flotho, P. and Nomura, S. and Kuhn, B. and Strauss, D. J.}, title = {Software for Non-Parametric Image Registration of 2-Photon Imaging Data}, year = {2022}, journal = {J Biophotonics}, doi = {https://doi.org/10.1002/jbio.202100330} }