neuroLIT

User Guide

  • Installation
    • Using Docker/Singularity (Recommended)
    • Using PyPI
    • From Source (Development Version)
    • System Requirements
    • Verifying Installation
    • Troubleshooting
      • Models Not Found
      • CUDA/GPU Issues
  • Usage Guide
    • Basic Usage
      • Running neuroLIT with Containerization
      • Running neuroLIT from PyPI
      • Mask Dilation
    • Understanding the Outputs
      • Output Files
      • File Structure
    • Advanced Usage
      • Direct Inpainting (Python API)
      • Batch Processing
    • Command-Line Interface Reference
      • lit-inpainting
      • lit-download-models
      • lit-postprocessing
    • Best Practices
      • Input Data
      • Performance
      • Quality Control
    • Postprocessing
      • Unified Postprocessing Script
      • Individual Postprocessing Tools
    • Common Issues
      • Poor Inpainting Quality
      • Mask Not Applied Correctly
      • Out of Memory Errors
  • Downstream Analysis with Lesion Exclusion
    • Surface-Based Analysis
    • Step 1 — Obtain Lesion Surface Labels
    • Step 2 — Extract Regional Statistics (Optional)
    • Step 3 — Project to Common Surface Template
    • Step 4 — Mask Lesion Vertices
    • Step 5 — Run Your Statistical Analysis
    • Step 6 — Visualize on the Cortical Surface (Optional)
    • Volumetric Analysis
      • Anatomy Reports
      • Excluding Affected Regions
      • Choosing Which Categories to Exclude
    • Dependencies
  • Training
    • Overview
    • Data Preparation
      • Conforming Images
      • Dataset Structure
    • Using Docker for Training

Development

  • Contributing
    • Contributing to neuroLIT
      • Getting Started
        • Setup
      • Types of Contributions
        • Bug Reports
        • Feature Requests
        • Code Contributions
        • Documentation
      • Development Guidelines
        • Code Style
        • Testing
        • Building Documentation
      • Git Workflow
        • Branch Naming
        • Pull Requests
      • Thank You!
  • API Reference
    • Command-Line Interface
      • run_lit()
      • Overview
      • Main Functions
        • lit-inpainting
      • Examples
        • Basic Usage
        • Programmatic Usage
    • Inference Module
      • InpaintingInferer
        • InpaintingInferer.__init__()
        • InpaintingInferer.__call__()
        • InpaintingInferer.sample_forward_diffusion()
        • InpaintingInferer.diffusion_forward()
        • InpaintingInferer.diffusion_backward()
      • SliceWiseInpaintingInferer
        • SliceWiseInpaintingInferer.__init__()
        • SliceWiseInpaintingInferer.get_slice_from_volume()
        • SliceWiseInpaintingInferer.slice_selector()
        • SliceWiseInpaintingInferer.get_inference_slices()
        • SliceWiseInpaintingInferer.__call__()
      • TwoAndHalfDInpaintingInferer
        • TwoAndHalfDInpaintingInferer.__init__()
        • TwoAndHalfDInpaintingInferer.view_agg_inference()
        • TwoAndHalfDInpaintingInferer.denoise()
        • TwoAndHalfDInpaintingInferer.__call__()
      • OffsetTwoAndHalfDInpaintingInferer
        • OffsetTwoAndHalfDInpaintingInferer.view_agg_inference()
        • OffsetTwoAndHalfDInpaintingInferer.__call__()
      • AnomalyInferer
        • AnomalyInferer.__call__()
        • AnomalyInferer.view_agg_inference()
        • AnomalyInferer.denoise()
      • DiffusionInfererVINN
        • DiffusionInfererVINN.__call__()
        • DiffusionInfererVINN.sample()
        • DiffusionInfererVINN.sample_backward_forward()
      • Overview
      • Key Concepts
    • Inpainting Module
      • resolve_inference_device()
      • dilate_mask()
      • conform_nifti()
      • get_slice_from_volume()
      • inpaint_volume()
      • main()
      • Overview
      • Main Function
        • main
      • Examples
        • Basic Inpainting
        • Custom Parameters
        • CPU Mode
      • Integration with Other Tools
        • Preprocessing Pipeline
        • Batch Processing
    • Data Module
      • Overview
      • Submodules
        • conform
        • datasets
        • transforms
      • Examples
        • Conforming Images
        • Using Datasets
        • Applying Transforms
        • Custom Transforms
        • Batch Conforming
    • Networks Module
      • Overview
      • Submodules
        • DiffusionUnet
        • interpolation_layer
      • Architecture Details
        • DiffusionUNet Architecture
      • Examples
        • Using DiffusionUNet
        • Loading Pre-trained Weights
        • Custom Model Configuration
        • Training Example
        • Model Summary
    • Postprocessing Module
      • Overview
        • Command-Line Usage
    • Utils Module
      • Overview
      • Submodules
        • download_checkpoints
        • plotting
      • Examples
        • Downloading Models
        • Command-Line Download
        • Getting Model Paths
        • Plotting Results
        • Plotting Slices
        • Mask Overlay
        • Custom Visualization
        • Batch Visualization
    • Quick Reference
      • Common Functions
      • Most Used Classes
      • Entry Points
  • Documentation Guide
    • Building the Documentation
      • Prerequisites
      • Build HTML
      • Clean Build Files
    • Writing Documentation
      • API Documentation
      • Adding New Pages

Reference

  • Citation
    • BibTeX
    • APA Format
    • Plain Text
neuroLIT
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© Copyright 2026, Clemens Pollak, David Kuegler, Martin Reuter et al..

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