Cell Event Importance Determination for Image Data Storage Optimization
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Solution Overview
Problem
The accumulation of large volumes of image data from long-term observation of cell motion or state changes in medical and life science fields overloads users, making it difficult to efficiently store and identify important cell-specific events.
Innovation Solution
An information processing device and system that determines the importance of cell-specific events using time-series image data and controls the acquisition process settings based on this determination, optimizing storage and data generation timelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If image data is accumulated over a long period for cell observation, then the observation duration is extended, but the storage capacity is exceeded and data management becomes difficult
Solution Approach 1:
The patent applies local quality by differentiating between important and unimportant image data based on cell-specific events. The determination unit analyzes each image to identify cell-specific events (division, death, differentiation) and assigns different importance levels, thereby selectively managing storage resources for different data types rather than treating all data uniformly.
Solution Approach 2:
The patent changes the parameter of data importance by analyzing temporal patterns and cell-specific events. The determination unit evaluates multiple parameters including time intervals between images, detected cell events, and morphological changes to dynamically assign importance levels, transforming static storage decisions into dynamic, parameter-based classifications.
2Reliability
If all image data is stored without selection, then no valuable events are missed, but storage capacity is strained and user workload increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically determine importance and select target images without user intervention. The determination unit autonomously analyzes image data, identifies cell-specific events, and controls the imaging device to capture only important frames, eliminating the need for users to manually review and select images from large datasets.
Solution Approach 2:
The system employs feedback by using determination results to dynamically adjust imaging parameters. The control unit receives feedback from the determination unit about which images are important and automatically adjusts acquisition settings, creating a closed-loop system that continuously optimizes data collection based on real-time analysis.
3Productivity
If manual determination of image importance is performed, then storage efficiency is improved, but user overload occurs during long-term observation
Solution Approach 1:
The patent replaces the mechanical system of manual user review with an automated information processing system. The determination unit uses image analysis algorithms to automatically assess importance, substituting human cognitive effort with computational processing that can handle large volumes of data without fatigue or overload.
Solution Approach 2:
The determination unit serves as an intermediary between the imaging device and storage system. It processes image data to extract meaningful information about cell-specific events and translates this into importance ratings, acting as a mediator that bridges raw data acquisition and intelligent storage management without requiring direct user involvement.
Data Source
AI summary
An information processing device according to the present technology includes a determination unit that determines importance related to a cell-specific event of a cell, using image data obtained from a time-series imaging process targeting the cell The information processing device also includes a control unit that controls a process regarding a setting for a target of acquisition of image data in the time-series imaging, on the basis of a determination result of the importance.