napari-dmc-brainmap

DMC-BrainMap is an end-to-end tool for multi-feature brain mapping across species.
This napari plugin was generated with Cookiecutter using napari's cookiecutter-napari-plugin template.

Quick start

A detailed guide and tutorial can be found on the Wiki pages of this repo.

Installation

DMC-BrainMap is a plugin for napari. There are two recommended installation paths, depending on whether you want to use the released plugin or develop the code.

Users

For reproducible regular use, first install napari by following the napari installation guide. Then install DMC-BrainMap from napari's graphical plugin manager:

Plugins > Install/Uninstall Plugins...

Search for napari-dmc-brainmap, then install it from the plugin manager. Napari handles the plugin installation graphically.

You can also install the released plugin with pip:

pip install napari-dmc-brainmap

After installation, open DMC-BrainMap from the napari plugin menu:

Plugins > dmc_brainmap

Developers

For troubleshooting and contributing to repo development, install the repository as an editable project with uv. In editable mode, code changes in this repository are picked up the next time napari is started with uv run napari.

First install uv if needed, following the uv installation guide.

Clone the repository:

git clone https://github.com/hejDMC/napari-dmc-brainmap.git
cd napari-dmc-brainmap

Sync the environment:

uv sync

This creates the project environment, installs Python 3.10 as required by the project, installs napari and all dependencies, and installs napari-dmc-brainmap from the local checkout.

Start napari:

uv run napari

Then find DMC-BrainMap from the napari plugin menu:

Plugins > dmc_brainmap

Usage

Please refer to the Wiki pages for detailed instructions and a short tutorial on how to use DMC-BrainMap. When working with DMC-BrainMap on your own data, please keep the following points in mind:

• DMC-BrainMap requires single-channel 16-bit .tif/.tiff images to work (in principle 8-bit also work)
• DMC-BrainMap requires that your data is organized by animals in separate folders (you can pool data later down the lane)
• DMC-BrainMap uses 5 channel labels (dapi, green, n3, cy3, cy5) corresponding to blue, green, orange, red and far red channels. However, these are only labels, you can assign them as you please. Hence, you can use DMC-BrainMap also for non-fluorescence data given you converted your images to single-channel 16-bit .tif/.tiff images. Please contact us if you need to use more than 5 channels.
• It is essential that you structure your data in the following way (hierarchical organization, same name for images in different channels, channel labels are selected by you), otherwise DMC-BrainMap won't work:

animal_id-001
│
└───stitched
│   │
│   └───dapi
│   |    │   animal_id-001_001.tiff
│   |    │   animal_id-001_002.tiff
|   │    |   animal_id-001_003.tiff
│   |    │   animal_id-001_004.tiff
│   |    │   ...
│   │   
│   └───green
│       │   animal_id-001_001.tiff
│       │   animal_id-001_002.tiff
│       │   animal_id-001_003.tiff
│       │   animal_id-001_004.tiff
│       │   ...
│   
animal_id-2
│   ...

Documentation

Documentation on DMC-BrainMap's source code can be found on the project's Read the Docs page.

Seeking help or contributing

DMC-BrainMap is an open-source project, and we welcome contributions of all kinds. If you have any questions, feedback, or suggestions, please feel free to open an issue on this repository.

License

Distributed under the terms of the BSD-3 license, "napari-dmc-brainmap" is free and open source software

Citing DMC-BrainMap

If you use DMC-BrainMap in your scientific work, please cite:

Jung, F., Cao, X., Heymans, L., Carlén, M. (2026) "DMC-BrainMap is an open-source, end-to-end tool for multi-feature brain mapping across species", Cell Reports Methods, https://doi.org/10.1016/j.crmeth.2026.101302

BibTeX:

@article{Jung2026a,
title = {DMC-BrainMap is an open-source, end-to-end tool for multi-feature brain mapping in different species},
journal = {Cell Reports Methods},
volume = {6},
number = {2},
pages = {101302},
year = {2026},
issn = {2667-2375},
doi = {https://doi.org/10.1016/j.crmeth.2026.101302},
url = {https://www.sciencedirect.com/science/article/pii/S2667237526000020},
author = {Felix Jung and Xiao Cao and Loran Heymans and Marie Carlén}
}