Large-Volume Cell Quality Evaluation With Error-Driven Re-Imaging
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Solution Overview
Problem
Existing cell quality evaluation methods struggle to accurately assess the quality of cells in large-volume cell-culture containers due to high estimation errors, which are exacerbated by the difficulty in imaging the entire container with conventional microscopes, and such assessments are costly and complex for ordinary users.
Innovation Solution
A cell quality evaluation apparatus and method that utilizes an information processing apparatus to set and adjust imaging regions based on feature quantity variations and container surface shape, performing re-imaging where necessary to maintain estimation accuracy, thereby reducing errors and improving assessment precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of imaging regions or area of imaging regions is increased to reduce estimation error, then measurement precision is improved, but device complexity and cost increase due to requiring larger or more microscopes
Solution Approach 1:
The cell culture container is divided into multiple imaging regions, and the system selectively images specific regions rather than requiring imaging of the entire container. This segmentation approach allows accurate quality estimation without needing to image the whole large-volume container, reducing the requirements for microscope size and complexity.
Solution Approach 2:
The system determines quality estimation accuracy for different imaging regions and selectively re-images regions with higher estimation error. This local quality approach focuses imaging resources on critical regions rather than uniformly imaging the entire container, improving accuracy without proportionally increasing device complexity.
2Measurement precision
If the number of imaging regions is increased to reduce estimation error, then measurement precision is improved, but loss of time increases due to more imaging operations required
Solution Approach 1:
The system calculates estimation error for each imaging region and selectively re-images only those regions with higher error, rather than re-imaging all regions. This approach reduces total imaging time while maintaining or improving overall quality estimation accuracy by focusing on critical regions.
Solution Approach 2:
The system performs partial re-imaging of only necessary regions rather than complete re-imaging of all regions. This partial action approach reduces imaging time while achieving sufficient accuracy by targeting only the regions that contribute most to error reduction.
3Ease of operation
If conventional microscopes are used for large-volume containers, then ease of operation is maintained, but measurement precision deteriorates due to inability to image entire container
Solution Approach 1:
The system segments the large-volume container into multiple imaging regions and uses conventional microscopes to image these regions selectively. This allows accurate quality estimation of large containers using conventional, easy-to-operate microscopes rather than requiring specialized large-volume imaging equipment.
Solution Approach 2:
The system introduces an information processing apparatus that acts as an intermediary between the conventional microscope and the quality evaluation. This intermediary calculates estimation errors, determines re-imaging needs, and integrates results from multiple regions, enabling accurate assessment with conventional equipment.
Data Source
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AI summary
There is provided a cell quality evaluation apparatus that evaluates a quality of cells based on a plurality of images acquired from an imaging apparatus that generates the plurality of images by imaging a plurality of imaging regions selected from entirety of a cell-culture container for culturing cells. The cell quality evaluation apparatus performs a process including estimation processing of estimating a quality of cells in the entirety of the cell-culture container by determining feature quantities from the plurality of images and calculating an average value of the feature quantities; derivation processing of deriving an estimation error of the quality estimated in the estimation processing, based on a variation of the feature quantities in the plurality of images and imaging information related to an area of the plurality of imaging regions; and imaging control processing of causing the imaging apparatus to perform re-imaging on at least one re-imaging region different from the plurality of imaging regions in a case where the estimation error is out of an allowable range.