Make Watershed BEM

Description

This app computes a watershed Boundary Element Model (BEM) surface from a FreeSurfer subject reconstruction, using MNE-Python's mne.bem.make_watershed_bem function (which wraps FreeSurfer's watershed algorithm). The FreeSurfer subject directory is copied into out_dir, the watershed BEM surfaces are generated there, and a QC report visualizing the BEM surfaces over the MRI is produced.

The app generates:

• The FreeSurfer subject reconstruction, copied into out_dir, with the generated .surf BEM surface files added to it
• An HTML QC report with MRI/BEM surface visualization

Inputs

• output (neuro/freesurfer): FreeSurfer subject reconstruction directory (recon-all output) to compute the watershed BEM surfaces from (required)

Outputs

• out_dir/: copy of the input FreeSurfer subject directory, including the generated watershed BEM .surf files
• out_report/report.html: QC report with MRI/BEM surface visualization
• product.json: metadata about the BEM computation

Configuration Parameters

key	type	default	description
output	string (path)	in_dir/freesurfer	Path to the FreeSurfer subject reconstruction directory (SUBJECTS_DIR)

Usage

Running on Brainlife.io

1. Go to the Make watershed BEM app page on brainlife.io.
2. Select a FreeSurfer reconstruction (neuro/freesurfer) as input.
3. Submit the process and retrieve out_dir (the FreeSurfer subject directory with its new BEM surfaces) and out_report/report.html once it completes.

Local Testing

# Create a config.json pointing to a FreeSurfer subject reconstruction directory
python3 main.py

Authors

• [Kami Salibayeva] (ksalibay@iu.edu)

Contributors

• [Kami Salibayeva] (ksalibay@iu.edu)
• [Maximilien Chaumon] (maximilien.chaumon@icm-institute.org)
• [Guiomar Niso]

Citations

1. Hayashi, S., Caron, B.A., Heinsfeld, A.S. et al. brainlife.io: a decentralized and open-source cloud platform to support neuroscience research. Nat Methods 21, 809–813 (2024). https://doi.org/10.1038/s41592-024-02237-2
2. Gramfort, A. et al. MEG and EEG data analysis with MNE-Python. Front. Neurosci. 7, 267 (2013). https://doi.org/10.3389/fnins.2013.00267

Funding Acknowledgement

brainlife.io is publicly funded and for the sustainability of the project it is helpful to Acknowledge the use of the platform. We kindly ask that you acknowledge the funding below in your code and publications. Copy and paste the following lines into your repository when using this code.

License

Copyright (c) 2026 MEEG Brainlife team. Licensed under AGPL-3.0, see license.txt.