Skullduggery Documentation

Welcome to Skullduggery! A command-line tool for automated defacing of anatomical MRI images in BIDS datasets.

Defacing removes identifiable facial features from anatomical neuroimaging data while preserving brain tissue, protecting participant privacy in neuroimaging studies.

Command-Line Usage

• Getting Started
  • Install
  • Your First Command
  • Common Commands
  • Next Steps
• Usage Guide
  • Installation
  • Basic Usage
  • Common Command-Line Options
  • Image Selection Filters
  • Advanced Options
  • Exit Codes
  • Tips and Best Practices
  • Getting Help
• Command Reference
  • Syntax
  • Required Arguments
  • Optional Arguments
  • Filter Format
  • Age Format
  • Exit Codes
  • Examples
  • Environment Variables
  • Tips
• Real-World Examples
  • Scenario 1: Quick Test on Single Participant
  • Scenario 2: Process Specific Cohort by Sessions
  • Scenario 3: Pediatric Data with Age-Based Templates
  • Scenario 4: Processing Only Specific Image Types
  • Scenario 6: DataLad-Managed Dataset with Git-Annex
  • Scenario 7: Force Re-indexing After Dataset Updates
  • Scenario 8: Batch Processing with Filtering
  • Scenario 9: Quality Assurance Review
  • Scenario 10: Using External Filter Configuration
  • Scenario 11: Processing with Debug Output
  • Scenario 12: Resuming Failed Processing
  • Post-Processing: Verification Scripts
  • Tips for Large-Scale Processing

Development

• Developer Guide
  • Quick Start
  • Project Structure
  • Development Tasks
  • Docstring Format
  • Adding Features
  • Dependencies
  • Debugging
  • Common Issues
  • Contributing

Indices and tables

• Index

• Module Index

• Search Page