Anterior Segment OCT Scleral Spur Positioning Automation
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Solution Overview
Problem
Conventional anterior segment three-dimensional image processing apparatuses require significant time and effort from operators to identify scleral spur positions in multiple two-dimensional tomographic images, making them inefficient for clinical use in angle analysis using optical coherence tomography (OCT).
Innovation Solution
An apparatus and method that automate the identification of scleral spur positions by specifying at least three positions in representative images, calculating a reference true circle, and adjusting spatial coordinates, allowing for automatic identification of positions in other images, reducing the need for manual input and increasing processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the operator manually inputs scleral spur positions in each two-dimensional tomographic image, then the accuracy of angle analysis is maintained, but the time required for processing increases significantly
Solution Approach 1:
The system performs preliminary automatic identification of scleral spur positions using image processing algorithms. By pre-processing the images to detect and mark candidate positions, the system reduces the manual workload while maintaining accuracy through subsequent verification steps.
Solution Approach 2:
The system enables self-service by automatically identifying scleral spur positions without requiring operator intervention in each image. The image processing apparatus autonomously analyzes the tomographic images, detects anatomical features, and generates ITC charts, making the system self-sufficient for routine processing tasks.
2Measurement precision
If the operator manually identifies scleral spur positions in multiple images, then processing accuracy is maintained, but the complexity of the operation increases
Solution Approach 1:
The system performs self-service by automatically identifying scleral spur positions and generating ITC charts without requiring complex manual operations. The image processing algorithms handle feature detection, position identification, and chart generation autonomously, simplifying the operator's role to oversight and verification.
Solution Approach 2:
The system replaces manual mechanical operations with automated image processing mechanisms. Instead of requiring operators to visually inspect and manually mark positions in multiple images, the system uses computational algorithms to detect anatomical features and generate results, substituting human effort with automated processing.
3Productivity
If automatic identification methods are used to reduce manual input, then processing time is reduced, but the precision of scleral spur position identification may be compromised
Solution Approach 1:
The system implements feedback mechanisms where the automatic identification results are verified and refined. The image processing apparatus uses iterative algorithms that compare detected positions with expected anatomical patterns, providing feedback to correct errors and improve accuracy while maintaining high processing speed.
Solution Approach 2:
The system uses advanced image processing mechanisms including edge detection, feature matching, and pattern recognition algorithms to automatically identify scleral spur positions with high precision. These computational mechanisms replace manual identification while maintaining or exceeding the accuracy of human operators through consistent algorithmic application.
Data Source
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Figure 3A~3B
AI summary
A processing apparatus is provided that receives and processes an anterior segment three-dimensional image of a subject's eye. The apparatus includes a first SS position specifying unit that accepts identification of at least three SS positions indicating a spatial coordinate position of a scleral spur (SS) of the subject's eye, using at least two representative images from among two-dimensional tomographic images constituting the three-dimensional image, a true circle calculating unit that calculates a reference true circle passing through the at least three SS positions, and a second SS position specifying unit that identifies the SS positions in non-representative images other than the representative images, based on the reference true circle.