Cell Image Evaluation Device Predicts Staining States
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Solution Overview
Problem
Current methods for evaluating the differentiation state of pluripotent stem cells through imaging and staining processes often lead to discrepancies, as the staining process is destructive and may not accurately reflect the cell's state, especially in layered cell regions where the staining marker fails to permeate, necessitating a non-destructive prediction method for timely subculturing and culture medium replacement.
Innovation Solution
A cell image evaluation device and program that uses machine learning to predict staining states by analyzing cell images before and after the staining process, integrating evaluation results from regions of interest to determine the staining states without actual staining, allowing for appropriate timing of operations like subculturing and culture medium replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If staining process is performed to evaluate cell differentiation state, then staining accuracy is improved, but cell viability deteriorates due to destructive nature of staining
Solution Approach 1:
The system performs preliminary imaging and evaluation of cell morphology features before the staining process to predict the staining outcome. This allows operators to assess cell differentiation state without actually performing the destructive staining procedure, thereby preserving cell viability while obtaining accurate differentiation information.
Solution Approach 2:
The system creates a virtual copy of the staining result by using machine learning models trained on paired data (pre-staining images and post-staining results). The model generates predicted staining outcomes from pre-staining images, eliminating the need for actual staining while providing equivalent information for differentiation assessment.
2Measurement precision
If staining process is performed on layered cell regions, then differentiation state can be evaluated, but staining penetration deteriorates causing inaccurate results
Solution Approach 1:
The system performs preliminary imaging and morphological analysis before staining to identify layered cell regions. By evaluating cell morphology features from pre-staining images, the system can predict staining outcomes and assess differentiation state without subjecting layered regions to inadequate staining penetration.
Solution Approach 2:
The system replaces the physical chemical staining process with an image analysis-based prediction system. Instead of relying on staining markers to penetrate cells (which fails in layered regions), the system uses machine learning models that analyze pre-staining morphological features to predict differentiation state, eliminating the penetration problem entirely.
3Reliability
If machine learning prediction is used to assess cell state, then cell viability is maintained, but prediction accuracy may deteriorate compared to actual staining
Solution Approach 1:
The system performs preliminary imaging and feature extraction before staining to train and validate prediction models. By using pre-staining images with carefully selected morphological features as input, the system achieves accurate differentiation prediction without compromising cell viability.
Solution Approach 2:
The system uses feedback from actual staining results (when performed) to continuously improve and retrain the prediction models. By comparing predicted staining outcomes with actual staining results, the system refines its prediction accuracy over time, ensuring reliable predictions while maintaining cell viability.
Data Source
AI summary
An evaluator 21 that evaluates states of cells included in each region of interest of a cell image, and a predictor 22 that performs, in advance, machine learning of a relationship between evaluation results for a specific region of interest within a first cell image obtained by imaging cells before a staining process and regions around the specific region of interest and staining states of cells of the specific region of interest within a second cell image obtained by imaging the same imaging targets as the cells of the first cell image after the staining process are provided. The predictor 22 predicts staining states of cells of a specific region of interest based on evaluation results for the specific region of interest and regions around the specific region of interest among the evaluation results for the third cell image of the cells before the staining process.