Digital Microscope Failure Detection and Reimaging Control
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Solution Overview
Problem
In digital microscope systems, failures during image capture often require time-consuming and labor-intensive reimaging processes, as issues are typically detected only after reviewing the images on a screen, leading to inefficiencies in pathology diagnosis and other applications.
Innovation Solution
An information processing apparatus and method that includes a detection unit to automatically identify failures in captured images and a generation unit to produce setting information for adjusting imaging conditions, enabling quicker and less effort-intensive reimaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual review of captured images is used to detect failures, then detection accuracy is maintained, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated image processing system that uses computer algorithms to detect failures. The detection unit automatically analyzes captured images to identify issues such as out-of-focus regions, contamination, or imaging errors, eliminating the need for manual screen review while maintaining detection accuracy.
Solution Approach 2:
The system enables self-service by allowing the imaging system to automatically detect and report its own failures without external human intervention. The detection unit continuously monitors captured images and automatically identifies problems, allowing the system to self-diagnose and trigger appropriate responses such as reimaging or alerting operators.
2Reliability
If comprehensive image evaluation is performed to detect all failure types, then detection reliability improves, but processing complexity increases
Solution Approach 1:
The patent segments the image evaluation process into multiple specialized detection modules, each targeting specific failure types such as focus issues, contamination, or artifacts. This modular approach allows comprehensive evaluation while managing complexity through organized, independent detection functions that can be selectively applied based on imaging conditions.
Solution Approach 2:
The system applies partial evaluation by selectively performing specific detection algorithms based on the imaging mode and risk assessment. Not all detection methods are applied to every image; instead, the system chooses appropriate evaluation levels, performing more intensive analysis only when necessary, thus balancing reliability with processing efficiency.
3Productivity
If automatic reimaging is implemented without setting information generation, then processing speed increases, but reimaging quality may deteriorate
Solution Approach 1:
The generation unit creates setting information in advance that optimizes reimaging parameters based on the detected failure type. Before performing automatic reimaging, the system prepares adjusted imaging conditions such as modified focus settings, illumination parameters, or acquisition settings tailored to prevent the same failure from recurring, ensuring high-quality results while maintaining fast processing.
Solution Approach 2:
The system implements feedback by using the failure detection results to automatically adjust reimaging settings. The generation unit analyzes the detected failure and generates optimized setting information that feeds back into the imaging process, creating a closed-loop system that continuously improves reimaging quality based on actual failure patterns observed.
Data Source
AI summary
Provided is art information processing apparatus including a detection unit configured to detect a failure requiring reimaging relating to an image captured using a digital microscope by evaluating the image, and a generation unit configured to, if the failure was detected by the detection unit, generate setting information for setting an imaging condition for during reimaging.