Information Processing for Image-Based Observation Grouping
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Solution Overview
Problem
Existing techniques for analyzing cell colonies struggle with efficiently processing large volumes of data from image-based observations, making it difficult for users to easily visualize and manage classification results.
Innovation Solution
An information processing device and method that acquires and highlights storage groups meeting user-defined classification criteria in a display image, allowing for easy visual confirmation and registration of similar observation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If image-based observation results are acquired for a plurality of target objects, then the quantity of data increases, but it becomes difficult for users to easily visualize and manage the analysis results
Solution Approach 1:
The patent segments the large volume of observation data by storing it in a container with multiple storages, where each storage holds a specific target object or group of objects. This segmentation allows users to navigate and visualize data in manageable units rather than overwhelming bulk data.
Solution Approach 2:
The patent introduces a new dimensional organization by grouping storages into categories based on classification criteria (such as cell type, treatment condition, or experimental parameters). This adds a hierarchical dimension to the data structure, enabling users to visualize and manage results through multiple levels of abstraction.
2Loss of information
If all observation results are displayed, then complete information is provided, but it becomes difficult to identify specific groups meeting classification criteria
Solution Approach 1:
The patent extracts and highlights only those storages that meet user-defined classification criteria from the complete set of observation results. This selective extraction allows users to focus on specific groups of interest while maintaining access to the complete dataset through the container structure.
Solution Approach 2:
The patent applies local quality by providing different display characteristics for different storages based on their classification attributes. Storages meeting specific criteria can be highlighted or marked differently, allowing users to quickly identify relevant groups without losing the context of the complete dataset.
3Measurement precision
If manual grouping of storages is performed, then precise classification is achieved, but the time and effort required increases significantly
Solution Approach 1:
The patent enables automatic grouping of storages based on classification criteria extracted from observation conditions or results. The system performs self-service classification by automatically comparing storage attributes against defined criteria and organizing them accordingly, eliminating the need for manual grouping while maintaining precise classification.
Solution Approach 2:
The patent utilizes parameter changes by automatically detecting and utilizing classification criteria from observation conditions or results to group storages. This automated parameter-based classification maintains precision while significantly reducing the time and effort required compared to manual grouping methods.
Data Source
AI summary
An information processing device includes: an acquirer that acquires observation results obtained by image-capturing a plurality of target objects under predetermined observation conditions; and a display controller that highlights, in a display image related to a container having a plurality of storages respectively storing the plurality of target objects, storages of the container that belong to a group meeting a classification criterion selected by a user.