Time-Series Cell Image Analysis for Live-Dead Discrimination
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Solution Overview
Problem
Existing cell image analysis methods using fluorescent images at specific times lead to inaccurate training data for machine learning models, resulting in poor performance in distinguishing live and dead cells.
Innovation Solution
A cell image analysis method that acquires a time-series cell image group using bright-field or phase contrast observation, extracts cell candidate regions, tracks these regions over time, and uses a trained machine learning model to analyze cell states based on fluorescence intensity features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If fluorescent images at specific times are used for training, then the training process is simple and quick, but the accuracy of distinguishing live and dead cells deteriorates
Solution Approach 1:
The patent performs preliminary actions by acquiring time-series images at multiple consecutive timings before the actual analysis. This allows the system to capture dynamic cellular changes over time, enabling more accurate training data collection without requiring complex real-time processing during the actual cell state determination.
Solution Approach 2:
The patent transitions from using single-time-point images to time-series images, adding the time dimension to the analysis. This dimensional change enables the system to capture temporal dynamics of cell states, improving discrimination accuracy between live and dead cells while maintaining manageable training complexity through structured time-series data collection.
2Ease of manufacture
If stained samples are used as labeled data, then the labeling process is straightforward, but the reliability of discriminating cell states deteriorates
Solution Approach 1:
The patent applies continuity of useful action by continuously acquiring images at multiple consecutive timings and consistently tracking the same cell regions throughout the time series. This continuous observation approach ensures that labeled data reflects genuine temporal changes in cell states, improving reliability while maintaining ease of data collection through automated imaging.
Solution Approach 2:
The system incorporates feedback mechanisms where region tracking results are used to verify and refine labeled data. By continuously monitoring cell regions across time points and comparing observations, the system can correct labeling errors and improve the reliability of training data while maintaining straightforward labeling procedures.
3Measurement precision
If region tracking over time is implemented, then the accuracy of identifying cell states is improved, but the complexity of the analysis process increases
Solution Approach 1:
The patent applies segmentation by dividing the time-series image analysis into distinct sequential steps: image acquisition at multiple timings, cell region extraction from each image, region tracking to identify corresponding regions across time points, and final cell state analysis. This segmentation reduces overall complexity by making each step manageable and automatable while maintaining high accuracy through the systematic progression through each phase.
Data Source
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AI summary
To provide a highly accurate cell image analysis method, provided is a cell image analysis method including: an image acquisition step of acquiring a time-series cell image group obtained by collecting a plurality of cell images obtained at a plurality of consecutive different timings in association with the timings; a region extraction step of extracting cell candidate regions from the cell images; a region tracking step of collecting, for the cell candidate regions over the plurality of cell images which are included in the time-series cell image group, the cell candidate regions determined to correspond to the same target in association with the timings, and acquiring the determined cell candidate regions as a time-series cell candidate region group; and an analysis step of analyzing information about a state of a cell, wherein the analysis step includes using a trained model.