Processing Workflows

This guide describes the typical image processing workflows available in KINTSUGI.

Overview

KINTSUGI provides a complete pipeline for multi-cycle immunofluorescence image analysis:

Illumination Correction - Fix uneven lighting across images
Stitching - Combine tiled images into single large images
Deconvolution - Remove optical blur and improve resolution
Extended Depth of Focus (EDoF) - Combine Z-stacks into 2D
Registration - Align images across multiple cycles
Autofluorescence Removal - Subtract background signal
Segmentation - Identify cells and structures
Spatial Analysis - Analyze spatial relationships

Workflow 1: Single Channel Evaluation

Use this workflow to test and tune parameters on a single channel before batch processing.

Notebook: notebooks/1_Single_Channel_Eval.ipynb

Steps:

Load a single channel image
Test illumination correction parameters
Test stitching parameters
Test deconvolution settings
Evaluate results visually

Workflow 2: Cycle Processing

Batch process all channels across multiple cycles.

Notebook: notebooks/2_Cycle_Processing.ipynb

Steps:

Configure input/output directories
Set processing parameters
Run illumination correction
Run stitching
Run deconvolution (optional)
Run EDoF (optional)
Run registration

Workflow 3: Signal Isolation

Remove autofluorescence and isolate true signal.

Notebook: notebooks/3_Signal_Isolation.ipynb

Steps:

Load registered images
Identify autofluorescence channels
Subtract autofluorescence
Apply filtering
Generate final signal images

Workflow 4: Segmentation Analysis

Perform cell segmentation and spatial analysis.

Notebook: notebooks/4_Segmentation_Analysis.ipynb

Steps:

Load processed images
Run InstanSeg segmentation
Extract cell features
Perform clustering
Analyze spatial relationships

Registration Module (Kreg)

The Kreg module provides image registration functionality based on VALIS.

Basic Usage

from kintsugi.kreg import Valis

# Initialize registrar
registrar = Valis(
    src_dir="/path/to/images",
    dst_dir="/path/to/output",
    reference_img_f="cycle1.tif",
    align_to_reference=True,
)

# Run registration
registrar.register()
registrar.register_micro()  # Fine registration
registrar.warp_and_merge_slides()  # Output registered images

Configuration Options

Parameter	Description	Default
`src_dir`	Source image directory	Required
`dst_dir`	Output directory	Required
`reference_img_f`	Reference image filename	Required
`max_image_dim_px`	Max image dimension for processing	2048
`compose_non_rigid`	Enable non-rigid registration	True
`crop_to_overlap`	Crop to overlapping region	True

Visualization Module (Kview2)

The Kview2 module provides interactive visualization tools.

Basic Usage

from kintsugi.kview2 import imshow, curtain, crop

# Display image
imshow(image)

# Compare two images with curtain view
curtain(image1, image2)

# Interactive crop
cropped = crop(image)

Stitching Module (Kstitch)

The Kstitch module provides tile stitching functionality.

Basic Usage

from kintsugi.kstitch import stitch_tiles

# Stitch tiled images
stitched = stitch_tiles(
    tile_dir="/path/to/tiles",
    output_path="/path/to/output.tif",
    overlap=0.1,  # 10% overlap
)

Tips for Large Datasets

Start small: Test parameters on a subset before full batch processing
Monitor memory: Use kintsugi info to check available resources
Use tiled processing: Enable tiled output for very large images
GPU acceleration: Enable CuPy for faster deconvolution