Fluorescence Imaging Autofluorescence Subtraction for Cell Segmentation
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Solution Overview
Problem
Current flow cytometry and fluorescence microscopy techniques face challenges in accurately segmenting cells due to high autofluorescence and background noise, leading to false negatives and false positives, and existing algorithms struggle with variations in cell types and image quality, resulting in suboptimal detection sensitivity and reliability.
Innovation Solution
A method involving calibration of the imaging system using unstained cells to account for autofluorescence and chromatic aberration, combined with High Dynamic Range Imaging (HDRI) and advanced image processing techniques like exposure fusion and inverse watershed transform for improved contrast and accurate cell segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If control sample subtraction is used to eliminate background signal and autofluorescence, then background noise is reduced, but measurement errors increase due to population variations and sample material differences
Solution Approach 1:
The patent performs autofluorescence measurement and subtraction on each individual cell image before analysis, rather than using a separate control sample. This preliminary action eliminates background noise specific to each cell while avoiding errors from population variations between separate samples.
Solution Approach 2:
The patent creates a digital copy of each cell's autofluorescence signal by measuring it in the same imaging session, then subtracts this copy from the total signal. This approach replaces physical control samples with digital replicas, eliminating variability between separate samples while maintaining accuracy.
2Measurement precision
If fluorescence intensity is increased to improve signal detection, then detection sensitivity improves, but background noise and autofluorescence also increase
Solution Approach 1:
The patent converts the harmful autofluorescence signal into a useful component by measuring it separately and subtracting it from the total signal. This allows the specific fluorescence signal to be detected with high sensitivity while eliminating the confounding background noise that would otherwise limit detection.
Solution Approach 2:
The patent segments the fluorescence signal into two distinct components: autofluorescence (measured without specific markers) and specific signal (total fluorescence minus autofluorescence). This segmentation allows each component to be analyzed separately, improving detection sensitivity while managing background noise.
3Productivity
If image processing algorithms are simplified for faster processing, then processing speed increases, but segmentation accuracy decreases due to inability to handle cell variations
Solution Approach 1:
The patent performs preliminary normalization of fluorescence signals by subtracting autofluorescence and correcting for exposure variations before segmentation. This pre-processing step standardizes the data, enabling simpler and faster segmentation algorithms to achieve high accuracy without needing to handle complex variations.
Solution Approach 2:
The patent transforms the image data by applying mathematical operations (autofluorescence subtraction, exposure normalization) that convert raw signals into standardized parameters. This parameter transformation simplifies subsequent processing while preserving segmentation accuracy across diverse cell types.
4Measurement precision
If exposure time is increased to capture low intensity signals, then detection sensitivity improves, but background signal accumulation increases
Solution Approach 1:
The patent performs preliminary measurement and subtraction of background autofluorescence signals acquired during the same exposure period. This allows long exposure times to be used for detecting low-intensity specific signals while the concurrently measured background is removed, preventing background accumulation from compromising detection.
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 enhances the signal-to-noise ratio and improves the accuracy of cell segmentation by effectively reducing background noise and autofluorescence, allowing for more sensitive and reliable cell analysis and detection.
Implementation Method 1
Both systems utilize fluorescent antibodies or other fluorescing probes (fluorophores) to tag cells having particular characteristics of interest and then detect the fluoresced light to locate the target cells.
Implementation Method 2
The fluorescence is typically detected optically by a device such as a photomultiplier tube (PMT)
Implementation Method 3
information about the cells of interest will typically be obtained optically
Data Source
AI summary
Systems and methods for automated, non-supervised, parameter-free segmentation of single cells and other objects in images generated by fluorescence microscopy. The systems and methods relate to both improving initial image quality and to improved automatic segmentation on images. The methods will typically be performed on a digital image by a computer or processor running appropriate software stored in a memory.