Cell Counting Method for 3D Clusters Without Disassembly
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Solution Overview
Problem
Conventional cell counting methods are inaccurate for floating cells, as they either underestimate the number of cells in clusters or overcount individual cells, and attempting to disassemble clusters to achieve accuracy can lower proliferation efficiency.
Innovation Solution
A computer-implemented method that separates images of cell clusters and individual cells, calculates their volumes by assuming spherical shapes and using correction coefficients, and divides the cluster volume by the individual cell volume to accurately count cells without disassembling clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cell clusters are pulled apart to achieve accurate cell counting, then measurement precision is improved, but proliferation efficiency deteriorates
Solution Approach 1:
The patent segments the cell cluster analysis into two distinct components: individual cell detection and cell cluster volume measurement. By separating these analysis tasks and applying different counting methodologies to each component, the system achieves accurate total cell counting without requiring physical disruption of cell clusters, thus maintaining proliferation efficiency while improving measurement precision.
2Ease of operation
If projected area method is used for cell counting, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent transitions from two-dimensional projected area parameters to three-dimensional volume parameters for cell cluster measurement. By calculating volume based on projected area and height information, and using spherical approximation for cell clusters, the method maintains operational simplicity while significantly improving counting accuracy for three-dimensional cell structures.
Data Source
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AI summary
To enable accurate counting the number of cultured cells without pulling apart cell clusters even if cells are aggregated densely to form three-dimensional cell clusters. An image of cultured cells is acquired, and from this image, an image of cell clusters and an image of individual cells are separated. Based on each of the image of cell clusters and the image of individual cells, the number of cells in the cell cluster and the number of individual cells are calculated.