Fluorescence Image Processing for Tissue Autofluorescence Removal
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Solution Overview
Problem
Existing methods struggle to effectively mitigate tissue autofluorescence in fluorescent-based imaging of cells and tissues, particularly in FFPE samples, which interferes with the detection of low-abundance signals in assays like immunofluorescence and FISH, and existing autofluorescence reduction techniques either dampen target signals or are not universally effective across different tissues.
Innovation Solution
A method involving imaging a sample to create a probe image and a background image, modifying the background image using image metrics to account for intensity and spatial discrepancies, and subtracting the adjusted background from the probe image to enhance target signals, utilizing techniques like White Top Hat algorithm, bandpass filtering, and image registration to compensate for intensity and spatial mismatches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If autofluorescence reduction treatments (Sudan Black, TrueBlack, TrueView) are applied, then background fluorescence is reduced, but target signal intensity is dampened or shifted
Solution Approach 1:
The patent segments the autofluorescence removal process into two distinct phases: (1) experimental phase where signal amplification and autofluorescence reduction are performed separately, and (2) computational phase where background subtraction is performed digitally. This segmentation allows optimal treatment for background reduction without compromising target signal, as the computational step can precisely subtract background without affecting target intensity.
Solution Approach 2:
The patent introduces computational image processing as an intermediary step between sample preparation and final analysis. By using algorithms to model and subtract autofluorescence background computationally, the method acts as a mediator that removes background without directly treating the sample, thereby preserving target signal integrity while achieving background reduction.
2Illumination intensity
If signal amplification (tyramide signal amplification) is used, then target signal intensity is boosted, but autofluorescence background also increases
Solution Approach 1:
The patent separates signal amplification from background reduction into distinct experimental and computational steps. Signal amplification is performed experimentally to boost target signal, while background subtraction is performed computationally to remove the concurrently amplified autofluorescence, preventing the harmful coupling of these effects.
Solution Approach 2:
The patent employs feedback mechanisms in the computational modeling step, where the system iteratively adjusts background subtraction parameters based on the actual observed background characteristics in the amplified signal, allowing precise removal of autofluorescence while preserving the amplified target signal.
3Ease of manufacture
If universal autofluorescence blocking procedure is applied, then processing is simplified, but effectiveness varies across different tissues
Solution Approach 1:
The patent transforms the static, fixed protocol approach into a dynamic, adaptive computational model that automatically adjusts to different tissue types. The background subtraction algorithm learns and adapts to tissue-specific autofluorescence characteristics, providing both simplicity of use and tissue-specific effectiveness without requiring manual protocol adjustment.
Solution Approach 2:
The patent enables automatic adjustment of processing parameters based on the specific tissue being analyzed. The computational model dynamically modifies background subtraction parameters according to tissue-type specific autofluorescence patterns, achieving both ease of use and high effectiveness across diverse tissue samples.
4Object-generated harmful factors
If spectral imaging and unmixing are used, then autofluorescence removal is achieved, but special high-end microscopic setups are required
Solution Approach 1:
The patent replaces complex mechanical/optical systems (spectral imaging hardware, unmixing algorithms) with a computational image processing approach that runs on standard equipment. By substituting hardware complexity with software-based background subtraction, the method achieves autofluorescence removal without requiring special microscopic setups.
Solution Approach 2:
The patent creates a computational model (digital copy) of the autofluorescence background that can be subtracted from the actual image. This virtual modeling approach replicates the effect of complex spectral unmixing without requiring the physical infrastructure, achieving the same result through computational means on standard equipment.
Data Source
AI summary
The present disclosure provides materials and methods related to image processing. In particular, the present disclosure provides methods for enhancing target signal detection using imaging processing analysis that identifies and removes non-specific background signals. The image processing methods of the present disclosure are useful for enhancing target signals in a variety of assays that involve fluorescent detection (e.g., fluorescent in situ hybridization, immunofluorescence).