Deconvolution Intensity Scaling - Preserving Quantitative Relationships

Experiment Overview

Item	Details
Date	2026-01-15
Goal	Fix pixel range scaling errors that break quantitative marker analysis
Environment	KINTSUGI KDecon module, HiPerGator, CuPy GPU
Status	RESOLVED

Context

After deconvolution, all channels were saturating at 65535 (max uint16) regardless of their original intensity ranges. This caused:

•Artificial clipping of bright regions
•Inverted relative intensity relationships between channels
•Broken quantitative marker expression analysis

Root Cause

The histogram clipping and rescaling logic in notebooks/Kdecon/main.py (lines 265-289) always stretched output to full 16-bit range:

python

# BROKEN CODE (before fix)
if raw_max <= 65535:
    scale = 65535  # <-- Always stretched to full range!
else:
    scale = raw_max
result = result * scale

Impact Analysis

Channel	Stage	Mean	Max	Problem
CH2 (weak)	Stitched	280	41,620	-
CH2 (weak)	Deconvolved	11,109	65,535	39.7x mean inflation!
CH1 (DAPI)	Stitched	3,998	58,214	-
CH1 (DAPI)	Deconvolved	7,927	65,535	1.98x mean inflation

Critical: The CH1/CH2 intensity ratio was inverted from 14.28 to 0.71.

Solution

Change the scaling to preserve original intensity relationships:

python

# FIXED CODE
# Scale to output range - preserve original intensity scale
# Using raw_max maintains relative intensity relationships between channels,
# which is critical for quantitative analysis of marker expression
scale = raw_max

result = result * scale

Fixed Results

Metric	Broken	Fixed
CH2 max	65,535 (saturated)	41,620 (preserved)
CH1 max	65,535 (saturated)	58,214 (preserved)
CH1/CH2 ratio	0.71 (inverted!)	1.00 (consistent)

Failed Attempts (Critical)

Attempt	Why it Failed	Lesson Learned
Assuming 65535 scaling was intentional	Destroyed quantitative relationships	Always check if scaling preserves relative intensities
Only checking max values	Missed the mean inflation and ratio inversion	Check both absolute AND relative intensity metrics
Ignoring hist_clip parameter	It normalizes to 0-1 then rescales	Understand the full pipeline before diagnosing
Looking only at single channel	Issue only visible when comparing channels	Multi-channel analysis reveals scaling bugs

Diagnostic Checklist

When deconvolved images show intensity issues:

•Check for saturation: np.sum(img >= 65535) should be near zero
•Compare channel ratios: Before/after deconvolution should be similar
•Check mean inflation: Deconvolved mean shouldn't be dramatically higher than input
•Trace the scaling pipeline: Input → deconv → clip → normalize → scale → output

Key Parameters

Parameter	Value	Description
`hist_clip`	0.01	Histogram clipping percentage (clips 0.01% at each end)
`raw_max`	varies	Maximum value from input image (used for output scaling)

Key Insights

•Histogram clipping normalizes to 0-1: The hist_clip parameter clips percentiles, then normalizes the result to 0-1 range. The final scaling determines the output range.
•Weak channels are most affected: Channels with low mean intensity show the largest inflation when stretched to full range.
•Relative relationships matter for quantitative analysis: Marker expression comparisons require consistent scaling across channels.
•Test with multiple channels: Single-channel testing won't reveal ratio inversion bugs.

Files Modified

•notebooks/Kdecon/main.py: Lines 283-288 (scaling logic)
•CLAUDE.md: Added documentation in Recent Enhancements section

References

•notebooks/Kdecon/main.py: KDecon class and decon() function
•notebooks/2_Cycle_Processing.ipynb: Deconvolution workflow
•Related skill: lightsheet-psf-deconvolution (PSF-related deconvolution issues)