Cell Evaluation Device Boundary Segmentation
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Solution Overview
Problem
Existing methods for evaluating individual cells in a cell group using cell images struggle with accuracy, particularly when cells are near boundaries between undifferentiated and differentiated states, leading to inappropriate evaluation results due to the influence of peripheral cells with similar characteristics.
Innovation Solution
A cell evaluation device and method that acquires a cell image, specifies an evaluation target cell and its peripheral cells, sets a boundary based on the cell group's state, and divides the image into regions to accurately identify peripheral cells, thereby evaluating the target cell with high precision by isolating cells in undifferentiated or differentiated states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If evaluation is performed using only individual cell images without considering peripheral cells, then evaluation simplicity is maintained, but evaluation accuracy deteriorates due to inability to capture cell state context
Solution Approach 1:
The evaluation process is segmented into distinct functional modules: image acquisition unit, boundary setting unit, cell specification unit, and evaluation unit. This segmentation allows the system to handle complex evaluation tasks involving peripheral cells and boundaries through organized, manageable components, resolving the contradiction between improved accuracy and increased complexity by structuring the complexity systematically
Solution Approach 2:
The boundary setting unit performs preliminary action by establishing boundaries between undifferentiated and differentiated cell regions before the evaluation process begins. This preliminary boundary establishment enables subsequent evaluation to focus on relevant peripheral cells within appropriate regions, improving accuracy while managing complexity through pre-processed spatial organization
2Measurement precision
If peripheral cells from both undifferentiated and differentiated regions are included in evaluation, then sufficient peripheral cells are available for evaluation, but evaluation accuracy deteriorates due to mixing of cells with different characteristics
Solution Approach 1:
The system applies local quality by differentiating evaluation criteria and peripheral cell selection based on spatial location relative to boundaries. Cells in undifferentiated regions are evaluated using criteria appropriate for undifferentiated cells, while cells in differentiated regions use different criteria. This local differentiation ensures that peripheral cells with similar characteristics are grouped together, improving evaluation accuracy while maintaining sufficient cell quantities within each homogeneous region
Solution Approach 2:
The cell population is segmented into undifferentiated and differentiated regions through boundary setting, creating distinct evaluation zones. This segmentation separates cells with different characteristics into different regions, allowing the system to select peripheral cells that are homogeneous in character while still providing sufficient quantities for reliable evaluation within each segmented region
3Measurement precision
If boundary setting is not performed, then evaluation process is simpler, but evaluation accuracy deteriorates due to inappropriate selection of peripheral cells near boundaries
Solution Approach 1:
The boundary setting unit performs preliminary action by establishing boundaries between undifferentiated and differentiated cell regions before evaluation begins. This pre-established boundary information is then used by the cell specification unit to appropriately select peripheral cells, ensuring that cells near boundaries are correctly classified and evaluated. The preliminary boundary setting resolves the contradiction by providing necessary spatial context without adding ongoing complexity to the evaluation process
Solution Approach 2:
The boundary setting unit acts as an intermediary between image acquisition and cell evaluation, providing essential spatial context and region classification. This intermediary function translates raw image data into structured boundary information that guides subsequent cell selection and evaluation, improving accuracy while managing complexity through a dedicated intermediate processing step
Data Source
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AI summary
The present invention provides a cell evaluation device, a cell evaluation method and a cell evaluation program capable of evaluating, when evaluating a predetermined evaluation target-cell using evaluation results of peripheral cells thereof, the evaluation target-cell with high accuracy. The cell evaluation device includes an image acquisition unit 30 that acquires a cell-image obtained by imaging a cell-group; a cell evaluation unit 32 that specifies an evaluation target-cell and peripheral cells around the evaluation target-cell in the cell-group, and evaluates the evaluation target-cell based on evaluation results of the peripheral cells; and a boundary setting unit 31 that sets a boundary in the cell-image based on a state of the cell-group, in which when specifying the peripheral cells, the cell evaluation unit 32 specifies only cells that are present in a divided region where the evaluation target-cell is present among plural divided regions divided by the boundary, as the peripheral cells.