dmc_brainmap
DMC-BrainMap is an end-to-end tool for multi-feature brain mapping across species
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. Hence, you first need to install napari and subsequently the DMC-BrainMap plugin via the plugin manager. To install napari, we recommend to install napari into a clean virtual environment using conda or venv. Please refer to the napari installation guide for more information and for information on installing napari as a bundled app.
Step 1: Setup the virtual environment (Python 3.10)
conda create -y -n napari-env -c conda-forge python=3.10
conda activate napari-env
Step 2: Install napari
python -m pip install "napari[all]"
Step 3: Install napari-dmc-brainmap
You can install napari-dmc-brainmap
via the napari plugin manager or via pip:
pip install napari-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. (2025) "DMC-BrainMap - an open-source, end-to-end tool for multi-feature brain mapping across species", bioRxiv, https://doi.org/10.1101/2025.02.19.639009
BibTeX:
@article{Jung2025x,
author = {Felix Jung and Xiao Cao and Loran Heymans and Marie Carlen},
doi = {10.1101/2025.02.19.639009},
journal = {bioRxiv},
month = {2},
title = {DMC-BrainMap - an open-source, end-to-end tool for multi-feature brain mapping across species},
url = {http://biorxiv.org/lookup/doi/10.1101/2025.02.19.639009},
year = {2025},
}
Version:
- 0.1.7b6
Last updated:
- 2025-08-13
First released:
- 2025-02-18
License:
- BSD-3-Clause
Operating system:
- Information not submitted
Requirements:
- numpy==1.26.4
- pandas==2.0.1
- matplotlib==3.8.3
- seaborn==0.12.2
- scikit-learn==1.4.1.post1
- scikit-image==0.22.0
- scikit-spatial==7.2.0
- tifffile==2023.2.28
- magicgui==0.8.1
- qtpy==2.4.1
- opencv-python==4.9.0.80
- natsort==8.4.0
- imagecodecs==2024.1.1
- mergedeep==1.3.4
- aicsimageio==4.14.0
- aicspylibczi==3.1.2
- aicssegmentation==0.5.3
- distinctipy==1.3.4
- bg_atlasapi==1.0.2
- shapely==2.0.1