Tissue Sample Image ROI Similarity Evaluation
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Solution Overview
Problem
Conventional techniques for evaluating tissue sample images do not effectively compare the selection of regions of interest (ROIs) by pathologists with those selected by apparatuses or other pathologists, making it difficult to determine if selected ROIs are important for diagnosis.
Innovation Solution
An information processing apparatus and method that inputs and compares ROIs from tissue sample images, calculating similarity based on correlations considering distances between regions, allowing for evaluation of the importance of selected ROIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional techniques are used to extract ROIs from tissue sample images, then ROI extraction can be performed automatically, but the correctness of selection cannot be evaluated by comparing with pathologist selections or other apparatus selections
Solution Approach 1:
The system calculates similarity between ROIs selected by different pathologists or between apparatus and pathologist selections, providing feedback on the correctness of automatic ROI extraction. This feedback mechanism enables evaluation of selection quality by comparing multiple selections and determining whether selected ROIs include important diagnostic regions.
2Measurement precision
If multiple ROI selections from different pathologists or apparatus are compared, then evaluation of selection correctness becomes possible, but the complexity of the system increases
Solution Approach 1:
The system introduces a similarity calculator as an intermediary component that objectively compares ROI selections from different pathologists or apparatus. This mediator computes similarity metrics based on positional relationships and diagnostic importance, enabling accurate evaluation without requiring complex direct comparison mechanisms between multiple selections.
3Measurement precision
If similarity calculation based on correlations considering distances between regions is implemented, then quantitative assessment of ROI importance becomes possible, but the computational complexity increases
Solution Approach 1:
The system changes the parameter of distance measurement by considering relative positional relationships between ROIs rather than absolute positions. The similarity calculator computes correlations based on distance metrics that reflect diagnostic importance, enabling quantitative assessment while managing computational complexity through parameter optimization.
Data Source
AI summary
This invention relates to an information processing apparatus which evaluates diagnosis based on the tissue sample image of a tissue. The information processing apparatus inputs a plurality of first regions selected as diagnosis targets from a tissue sample image obtained by capturing a tissue, a plurality of second regions selected as diagnosis targets from the tissue sample image, and pieces of position information of the respective regions selected on the tissue sample image. The information processing apparatus calculates a similarity between the plurality of first regions and the plurality of second regions based on correlations considering the distances between the selected regions on the tissue sample image. This arrangement can evaluate whether ROIs selected by a pathologist or apparatus include an important region of interest.