skullduggery 0.0.2rc98.post1

Sample Python Project for creating a new Python Module

Skullduggery

Skullduggery is a BIDS app for automated defacing of anatomical MRI images. It removes identifiable facial features from anatomical neuroimaging data while preserving brain tissue, protecting participant privacy in neuroimaging studies.

Features

• Automated defacing - Uses template-based registration and hard-coded anatomical markers
• Age-specific templates - Supports pediatric cohorts with age-based template selection from TemplateFlow
• BIDS-compatible - Full integration with BIDS datasets and metadata
• Multi-series support - Process multiple anatomical series in a single run
• Quality assurance - Generates HTML reports with mosaic visualizations
• DataLad integration - Optional support for git-annex metadata tracking
• Flexible filtering - Select specific participants/sessions with BIDS filters

Installation

Basic Installation

pip install skullduggery

With DataLad Support

For optional git-annex metadata tracking:

pip install skullduggery[datalad]

Development Installation

pip install -e ".[test]"

Quick Start

Basic Usage

skullduggery /path/to/bids/dataset

Advanced Usage

skullduggery /path/to/bids/dataset \
  --participant-label 01 02 03 \
  --template MNI152NLin2009cAsym \
  --save-all-masks \
  --report-dir ./defacing_reports

Command-Line Options

Required Arguments

• BIDS_PATH: Path to the BIDS dataset directory

Optional Arguments

• --participant-label - Process specific participants (space-separated IDs, prefix 'sub-' optional)
• --session-label - Process specific sessions (space-separated IDs, prefix 'ses-' optional)
• --template - TemplateFlow template name (default: MNI152NLin6Asym)
• --default-age VALUE:UNIT - Fallback age for participants missing age data (e.g., 5:years)
• --save-all-masks - Save defacing masks for all series (default: only reference series)
• --report-dir DIR - Directory for HTML reports and visualizations
• --ref-bids-filters JSON - BIDS filters for selecting reference image (default: {"suffix": "T1w", "datatype": "anat"})
• --other-bids-filters JSON - BIDS filters for images to deface (default: [{"datatype": "anat"}])
• --datalad - Enable DataLad integration for metadata tracking
• --deface-sensitive - Only deface images marked with distribution-restrictions metadata
• --force-reindex - Force pyBIDS database reindexing
• --debug LEVEL - Set logging level (default: info)

How It Works

The defacing pipeline follows these steps:

1. Template Selection - Loads appropriate template from TemplateFlow (age-specific if available)
2. Reference Registration - Registers participant's reference image to template space
3. Mask Warping - Warps template-space defacing mask to participant's native space
4. Series Defacing - Applies mask to all anatomical series for the participant
5. Report Generation - Creates HTML reports with QA visualizations
6. Optional Commit - Saves changes to DataLad repository if enabled

Preprocessing Requirements

Before running skullduggery, ensure your dataset:

• Follows the BIDS standard
• Contains valid participants.tsv (with optional age column for pediatric templates)
• Has anatomical images in proper BIDS format

Output Files

Skullduggery creates:

• Defaced images - In-place modification of anatomical NIfTI files
• Defacing masks - *desc-deface_mask.nii.gz files (if requested)
• Transformation matrices - *_from-T1w_to-<template>_xfm.mat files
• HTML reports - With registration and defacing visualizations
• Metadata - DataLad metadata updates (if enabled)

Examples

Process All Participants

skullduggery /data/mybids

Process Specific Pediatric Cohort

skullduggery /data/mybids \
  --participant-label 001 002 003 \
  --template MNIInfant \
  --default-age 6:months

Generate Detailed Reports

skullduggery /data/mybids \
  --save-all-masks \
  --report-dir ./qa_reports \
  --debug debug

With Distribution Restrictions Filtering

skullduggery /data/mybids \
  --deface-sensitive \
  --datalad

Documentation

• Developer Guide - Development setup and guidelines
• VSCode Setup - Visual Studio Code configuration
• API Documentation - Detailed API reference

Contributing

See CONTRIBUTING.md for guidelines on reporting issues and contributing code.

License

This project is licensed under the MIT License. See LICENSE for details.

Citation

If you use Skullduggery in your research, please cite:

@software{skullduggery,
  title={Skullduggery: Automated Defacing of Neuroimaging Data},
  author={Pinsard, Basile},
  url={https://github.com/UNFmontreal/skullduggery},
  year={2024}
}

Support

For issues, questions, or suggestions, please open an issue on GitHub.