Spectral Unmixing for Fluorescence Microscopy Images
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Solution Overview
Problem
Fluorescence microscopy images often contain broadband signals like autofluorescence and DAPI, which are noisy and overwhelm target signals such as quantum dots, making spectral unmixing resource-intensive and resulting in imperfect results due to varying sample types and broadband signatures that are assumed to be fixed throughout the image.
Innovation Solution
The method involves identifying and generating reference signals from broadband signals in the image, using them to unmix selected regions containing target signals, and applying a non-negative linear least-squares method to separate component fluorescent channels, while ignoring or tagging predominantly broadband regions to exclude them from the unmixing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spectral unmixing is applied to each pixel using a linear equation solver, then complete signal separation is achieved, but computational resources are excessively consumed and processing time increases
Solution Approach 1:
The patent divides the image into two distinct segments: predominantly broadband regions and other regions. By segmenting the processing domain, the method applies different unmixing strategies to different regions, thereby reducing overall computational burden while maintaining accuracy where needed.
Solution Approach 2:
The patent extracts and removes broadband signals (such as autofluorescence and DAPI) from the spectral mixture before applying unmixing to the remaining regions. This extraction eliminates the dominant broadband component that would otherwise consume excessive computational resources during unmixing.
2Productivity
If fixed reference spectra are used for broadband signals throughout the image, then unmixing computation is simplified, but accuracy deteriorates due to variations in sample types and locations
Solution Approach 1:
The patent implements dynamic reference spectra that adapt to different regions and sample types. Instead of using fixed reference spectra, the system adjusts the broadband reference spectra based on the specific characteristics of each region, thereby maintaining accuracy across diverse samples while managing computational complexity.
Solution Approach 2:
The patent applies different reference spectra and unmixing parameters to different regions of the image based on their specific characteristics. By making the reference spectra local rather than universal, the method achieves accurate unmixing for each region while avoiding the computational burden of processing every pixel with the same complex model.
3Measurement precision
If broadband signals like autofluorescence and DAPI are included in spectral unmixing, then complete signal decomposition is achieved, but target signals are overwhelmed and diagnostic quality decreases
Solution Approach 1:
The patent extracts broadband signals (autofluorescence, DAPI, red blood cells) from the spectral mixture and removes them as separate components. By taking out these dominant broadband signals, the target signals (such as quantum dots) are no longer overwhelmed and become visible and diagnostically useful.
Solution Approach 2:
The patent converts the harmful effect of broadband signals overwhelming target signals into a benefit by using the broadband regions themselves to generate reference spectra. The predominantly broadband regions, which were previously problematic, are now utilized to create accurate reference models that improve the unmixing of target signals in other regions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the generation of images consisting mainly of desired target signals without broadband noise, facilitating efficient image analysis and accurate diagnoses by selectively unmixing only relevant regions, reducing computational intensity, and improving diagnostic precision.
Implementation Method 1
fluorescence microscopy is used to generate images of biological specimens which are stained with one or more fluorophores
Implementation Method 2
broad absorption spectra
Implementation Method 3
spectral unmixing the resulting image or portions thereof. This is a standard linear algebra problem that is properly applied to positive (or non-negative) signals
Data Source
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AI summary
Processing of images acquired via fluorescence microscopy by identifying broadband and other undesired signals from the component signals of a scanned image, and processing selected regions of the image that are known to contain signals of interest, thereby extracting or identifying desired signals while subtracting undesired signals. One or more broadband signals are recognized by their unique signature and ubiquitous dispersion through the image. Regions of the scanned image may be tagged as consisting of predominantly broadband signals and are ignored during a spectral unmixing process. The remaining regions of the image, or selected regions of the image known to contain desired signals, may be unmixed, and the plurality of reference spectra subtracted from the components to extract or identify the target signals. The set of target signals may be refined by eliminating known or obvious sources of noise by, for instance, being compared to known or ideal sets of signals from similar materials.