KINTSUGI Documentation
Knowledge Integration with New Technologies for Simplified User-Guided Image processing
Multiplex image processing for challenging datasets with a focus on user integration rather than automation. This pipeline includes 2D/3D GPU/CPU illumination correction, stitching, deconvolution, extended depth of focus, registration, autofluorescence removal, segmentation, clustering, and spatial analysis.
Citation
Smith, J. A. et al. Protocol for processing and analyzing multiplexed images improves lymphatic cell identification and spatial architecture in human tissue. STAR Protocols 6, 103976 (2025).
Quick Links
Getting Started
User Guide
- Processing Workflows
- Overview
- Workflow 1: Single Channel Evaluation
- Workflow 2: Cycle Processing
- Workflow 3: Signal Isolation
- Workflow 4: Segmentation Analysis
- Registration Module (Kreg)
- Visualization Module (Kview2)
- Stitching Module (Kstitch)
- Workflow 5: Quality Control
- Denoising Module
- Segmentation Module
- HPC/SLURM Batch Processing (Snakemake)
- Tips for Large Datasets
- Signal Isolation
- Command Line Interface
- API Reference
- Main Package
- Kreg Module (Registration)
- Kview2 Module (Visualization)
- Kstitch Module (Stitching)
- signal Module
- qc Module
- denoise Module
- edf Module (Extended Depth of Focus)
- kcorrect_gpu Module (Illumination Correction)
- project Module
- gpu Module
- hpc Module
- parallel_io Module
- zarr_io Module
- deps Module
- cli Module
Technical Notes
- Algorithm Optimization Plan: BM3D-lite & NLM Denoising
- Algorithm Optimization Validation Results
- KINTSUGI Performance Audit Report
- Evaluation: PyImageJ/CLIJ2 vs Pure Python for Extended Depth of Field (EDF)
- Kstitch Alternatives Evaluation
- KINTSUGI Processing Speed Optimization - Session Summary
- Bug Report:
UnboundLocalErrorinregistration.py:register_micro()(v1.2.0)