Contextual Spectrum Mask Generation for Label-Free Cell Viability

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Solution Overview

Problem

Traditional approaches for determining the condition of biostructures, such as cell viability and cell cycle analysis, rely heavily on fluorescence microscopy, which faces limitations like photobleaching, chemical toxicity, and weak fluorescent signals, making it difficult to study live cell cultures over extended periods without harming them.

Innovation Solution

The use of quantitative phase imaging (QPI) combined with deep learning techniques to generate contextual masks, allowing for label-free imaging and classification of cell states without the need for fluorescent dyes, using neural networks to analyze quantitative image data and provide context-specific information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If fluorescence microscopy is used to determine cell viability and cell cycle stages, then measurement precision is improved, but object-affected harmful factors worsen due to photobleaching, chemical toxicity, and phototoxicity

Engineering Contradiction:

Improvecell viability assessment accuracyVSAvoidphototoxicity and chemical toxicity

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent replaces the optical/chemical fluorescence detection system with a machine learning-based classification system that processes quantitative phase imaging data. The neural network model learns to classify cell states from phase images without requiring fluorescent labels, thereby eliminating phototoxicity and chemical toxicity while maintaining measurement precision through computational analysis of cellular morphological features

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces quantitative phase imaging as an intermediary measurement technique that captures cellular information without the harmful effects of fluorescence. The phase imaging data serves as a safe intermediary that contains sufficient information for cell state classification, which is then processed by machine learning algorithms to achieve accurate viability assessment without direct fluorescent labeling

Inventive Principle:

Principle #24Intermediary (Mediator)

2Duration of action of stationary object

If fluorescence microscopy is used for extended period monitoring, then duration of action is improved, but reliability worsens due to photobleaching and cell harm

Engineering Contradiction:

Improvelong-term cell monitoring durationVSAvoidcell viability assessment reliability

Core Design Contradiction:

Duration of action of stationary objectVSReliability

Solution Approach 1:

The patent replaces continuous fluorescence imaging with machine learning-based classification of quantitative phase images. This substitution allows for extended monitoring periods because phase imaging does not cause photobleaching or phototoxicity, thereby maintaining both the duration of monitoring and the reliability of cell viability assessment over time

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent enables continuous, long-term monitoring of cell cultures by using a non-invasive phase imaging technique combined with machine learning classification. The system can continuously acquire phase images and classify cell states without the accumulating damage that limits fluorescence-based methods, thus maintaining reliable measurements throughout extended experimental durations

Inventive Principle:

Principle #20Continuity of useful action

3Measurement precision

If fluorescent dyes are used for cell labeling, then measurement precision is improved, but loss of substance worsens due to chemical toxicity

Engineering Contradiction:

Improvecell state classification accuracyVSAvoidchemical reagent toxicity

Core Design Contradiction:

Measurement precisionVSLoss of substance

Solution Approach 1:

The patent replaces chemical fluorescent labeling with a computational approach that extracts cell state information from quantitative phase images. The machine learning model processes morphological features visible in phase images to achieve accurate cell cycle and viability classification without requiring any chemical reagents, thereby eliminating chemical toxicity while maintaining measurement precision

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20220383986A1Complex System for Contextual Spectrum Mask Generation Based on Quantitative Imaging

Publication Date: 2022.12.01 THE BOARD OF TRUSTEES OF THE UNIV OF ILLINOIS

AI summary

Methods, apparatus, and storage medium for determining a condition of a biostructure by a neural network based on quantitative imaging data (QID) corresponding to an image of the biostructure. The method includes obtaining specific quantitative imaging data (QID) corresponding to an image of a biostructure; determining a context spectrum selection from context spectrum including a range of selectable values by: applying the specific QID to an input layer of a context-spectrum neural network, wherein the context-spectrum neural network is trained, according to a combination of focal loss and dice loss, based on previous QID and constructed context spectrum data associated with the previous QID; mapping the context spectrum selection to the image to generate a context spectrum mask for the image; and determining a condition of the biostructure based on the context spectrum mask.