Fluorescence Image Unmixing for Spectral Crosstalk Removal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing spectral unmixing methods in fluorescence microscopy struggle with spectral crosstalk, leading to inaccurate identification and quantification of fluorescent labels due to non-linear effects and assumptions of linearity and pixel independence, which are exacerbated by quenching and photobleaching.
Innovation Solution
A method involving pixel-based unmixing followed by channel-specific object unmixing, where conditions are applied to remove objects resulting from spectral crosstalk while preserving true signals, using estimated abundances and known signal sources to refine image segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If linear unmixing is used to extract fluorophore abundances from spectral images, then the computational simplicity and ease of implementation are improved, but the measurement precision deteriorates due to assumptions of linearity and pixel independence that cannot account for non-linear effects like quenching and photobleaching
Solution Approach 1:
The patent transforms the unmixing problem from direct spectral unmixing to a two-stage process: first performing object segmentation to identify spatial regions, then performing unmixing at the object level rather than pixel level. This parameter change in the processing hierarchy allows incorporation of spatial context and non-linear effects while maintaining computational feasibility.
Solution Approach 2:
The patent divides the image processing into distinct segmentation and unmixing stages. By segmenting objects first and then performing unmixing on segmented regions rather than individual pixels, the method captures spatial relationships and non-linear effects that pixel-by-pixel linear unmixing misses, thereby improving accuracy while remaining computationally tractable.
2Adaptability or versatility
If spectral unmixing methods are applied to resolve overlapping fluorophore signals, then the ability to distinguish different labels is improved, but the reliability deteriorates when non-linear effects such as quenching and photobleaching are present
Solution Approach 1:
The patent introduces dynamic, adaptive processing by performing unmixing at the object level after segmentation, allowing the method to adapt to local spatial variations and non-linear effects. This dynamic approach contrasts with static pixel-by-pixel linear unmixing and enables reliable discrimination of fluorophores even when quenching or photobleaching occurs.
Solution Approach 2:
The patent performs object segmentation as a preliminary action before unmixing. This preliminary spatial organization allows the subsequent unmixing step to operate on meaningful biological units rather than arbitrary pixels, improving reliability by accounting for spatial context and non-linear effects that occur at the object level.
3Productivity
If image segmentation is performed on each single channel image individually, then the processing speed is improved, but the manufacturing precision deteriorates due to spectral crosstalk causing inaccurate object identification
Solution Approach 1:
The patent segments the processing into two distinct stages: first performing rapid individual channel segmentation to identify candidate objects, then performing corrective unmixing at the object level to remove spectral crosstalk artifacts. This two-stage segmentation approach maintains processing speed while improving identification accuracy.
Solution Approach 2:
The patent extracts and removes objects identified as spectral crosstalk artifacts from the segmentation results. By identifying objects that appear in multiple channels and using unmixing to determine their true spectral composition, the method extracts false positives caused by crosstalk while preserving true objects, thereby improving precision without sacrificing the speed of individual channel processing.
Data Source
AI summary
Methods, systems, computer programs and computer readable media comprising instructions for processing images and for analyzing samples are described, wherein the processing comprises performing spectral unmixing to identify regions of an image associated with one or more signals such as, e.g., fluorescent signals, luminescent signals, and/or colorimetric signals. The invention finds applications in the context of analysis of labelled samples, such as, e.g., for the purpose of detecting and/or quantifying molecules, molecular complexes, cells, subcellular structures, microorganisms, etc.