Endoscope Size Measurement Using Time-Series Representative Values
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Solution Overview
Problem
Existing medical imaging technologies struggle to accurately ascertain the size of an observation target region in medical video images due to instability caused by body movement and camera shaking, leading to potential erroneous measurements.
Innovation Solution
A medical support device and method that utilizes a processor to acquire and output size-related information in a time series, including representative values such as maximum, minimum, average, and variance of sizes, and stabilizes the measurement by assessing the stability of the size changes using AI-based recognition and distance information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional medical imaging is used to measure the size of observation target regions, then the measurement process is simple, but the measurement precision deteriorates due to instability from body movement and camera shaking
Solution Approach 1:
The system performs preliminary actions by acquiring multiple images before final measurement, capturing temporal variations caused by movement. It also preliminarily identifies corresponding regions across frames to establish a basis for stable measurement that accounts for motion artifacts.
Solution Approach 2:
The system maintains continuous useful action by processing a time series of images rather than relying on a single snapshot. This continuous observation across multiple frames allows the system to distinguish between movement artifacts and actual size changes, improving both precision and reliability.
2Measurement precision
If multiple images are acquired to improve measurement accuracy, then the measurement precision improves, but the loss of time increases due to processing multiple frames
Solution Approach 1:
The system extracts only the essential information needed for measurement from the time series of images. By focusing on identifying corresponding regions and calculating size variations rather than processing all image data equally, it achieves accurate measurement while reducing unnecessary computational overhead.
Solution Approach 2:
The system changes parameters by analyzing size measurements across multiple time points rather than processing complete images. This parameter transformation from full image processing to specific measurement point analysis reduces processing time while maintaining precision.
3Ease of operation
If size measurements are taken from individual frames, then the ease of operation is high, but the reliability deteriorates due to movement-induced errors
Solution Approach 1:
The system implements feedback by comparing size measurements across multiple frames and identifying consistent patterns. This feedback mechanism allows the system to distinguish between measurement variations caused by movement versus actual size changes, improving reliability while maintaining operational simplicity through automated processing.
Solution Approach 2:
The system performs self-service by automatically identifying corresponding regions and calculating size variations without requiring manual intervention. This self-automated process maintains ease of operation while improving reliability through consistent, repeatable measurement procedures across multiple frames.
Data Source
AI summary
A medical support device includes a processor. The processor is configured to: acquire size-related information that is information corresponding to sizes in a time series of an observation target region shown in a medical video image; and output the size-related information. A representative value of the sizes in the time series is used as the size-related information.