Machine-Learned Cell Counting and Confluence for Multiple Cell Types
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Solution Overview
Problem
Existing methods for determining the number and degree of confluence of cells in microscopy images are inaccurate, time-consuming, and require expert knowledge, leading to potential errors and inefficiencies in cell culture analysis.
Innovation Solution
A computer-implemented method using machine-learned algorithms to analyze light-microscope images, adapting image size, and determining cell counts and confluence levels, with optional cross-plausibilization and density mapping for multiple cell types, enabling automated and accurate estimations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual counting techniques are used to determine cell number and confluence, then measurement precision can be maintained, but productivity is significantly reduced due to time-intensive analysis
Solution Approach 1:
The patent replaces manual mechanical counting and visual estimation with an automated image analysis system that processes microscopy images using computer algorithms. The system automatically detects cell boundaries, counts cells, and calculates confluence percentages from digital images, eliminating the need for manual intervention while maintaining or improving measurement accuracy.
Solution Approach 2:
The image analysis system performs self-service by automatically processing images without requiring expert knowledge or manual parameter adjustment. The algorithm independently identifies cells, handles varying image qualities, and produces results without human intervention, thereby increasing productivity while preserving measurement precision through consistent automated evaluation.
2Productivity
If threshold value-based automated techniques are used for cell analysis, then productivity is improved through automation, but measurement precision deteriorates due to parameterization requirements and susceptibility to errors
Solution Approach 1:
The patent employs parameter changes by using adaptive thresholding methods that automatically adjust detection parameters based on image characteristics rather than requiring fixed, pre-set values. The system analyzes local contrast variations and adapts thresholds dynamically to different regions and imaging conditions, eliminating the need for expert parameterization while maintaining high detection accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where the analysis results are continuously refined based on image data quality assessment. The algorithm evaluates local image characteristics and adjusts its detection parameters accordingly, creating a closed-loop system that improves measurement precision through automatic feedback rather than relying on static, error-prone parameter settings.
3Measurement precision
If expert knowledge is required for parameterization of evaluation algorithms, then measurement precision can be optimized, but ease of operation is reduced and productivity is slowed down
Solution Approach 1:
The evaluation algorithm performs self-service by automatically determining optimal parameters from the image data itself without requiring expert knowledge for manual configuration. The system independently assesses image quality, cell density, and contrast characteristics to adaptively set detection parameters, making the system easy to operate while maintaining high measurement precision through automated parameter optimization.
Data Source
AI summary
Various examples of the disclosure relate to techniques to count cells in a microscopy image and/or to determine a degree of confluence of the cells in the microscopy image. To that end, machine-learned algorithms are used.