Automated Angiographic Image Analysis for Vascular Leakage Quantification
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Solution Overview
Problem
Current methods for assessing vascular leakage in retinal and choroidal vessels are subjective and lack objective, quantitative measures, limiting their utility as diagnostic and therapeutic biomarkers for conditions like uveitis, where inflammation affects blood vessel flow and dye leakage.
Innovation Solution
An automated imaging system that captures and analyzes angiographic images using contrast dyes to quantify vascular leakage through pixel analysis, pattern recognition, and classification techniques, enabling objective assessment and monitoring of disease activity and treatment response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement or qualitative assessment of vascular leakage is used, then the assessment process is simple, but the measurement precision and objectivity are insufficient
Solution Approach 1:
The patent replaces manual visual assessment and subjective interpretation with an automated computer-based image analysis system. The system uses digital image processing algorithms to objectively quantify vascular leakage by analyzing angiographic images, substituting the mechanical/manual process with an automated computational approach that provides precise pixel-based measurements of leakage area and intensity.
Solution Approach 2:
The image analysis system performs self-assessment by automatically processing angiographic images to quantify vascular leakage without requiring manual measurement. The system independently executes image registration, difference calculation, and leakage quantification algorithms, enabling objective assessment while reducing reliance on subjective human interpretation.
2Reliability
If automated image analysis with pattern recognition is implemented, then objective quantitative assessment is achieved, but the device complexity and processing requirements increase
Solution Approach 1:
The patent segments the image analysis process into distinct functional modules: image registration to align sequential angiographic frames, difference calculation to identify changing regions, and pattern recognition to classify leakage patterns. This segmentation allows each module to be optimized independently while working together to provide reliable automated assessment of vascular leakage.
Solution Approach 2:
The system introduces an intermediary computer-based analysis layer between the raw angiographic images and the clinical assessment. This intermediary performs automated image processing, calculates pixel differences between time points, and generates quantitative leakage metrics, serving as a mediator that transforms subjective visual assessment into objective reliable measurements.
3Productivity
If subjective interpretation of angiographic patterns is used, then the analysis process is quick, but the reproducibility and objectivity are limited
Solution Approach 1:
The patent replaces the mechanical process of manual visual inspection with an automated digital image analysis system that processes angiographic images computationally. This substitution maintains rapid assessment capability while simultaneously providing precise pixel-based quantification of vascular leakage, eliminating the trade-off between speed and precision inherent in manual methods.
Solution Approach 2:
The system enables continuous automated analysis of angiographic sequences by processing multiple time-point images through registration and difference calculation algorithms. This continuous processing maintains high productivity by automatically analyzing the entire angiographic series without manual intervention while generating precise quantitative leakage measurements at each time point.
Data Source
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AI summary
Systems and methods are provided for automated analysis of angiographic images. An angiographic imaging system is configured to capture a first image of a region of interest, representing a first time, and a second image of a region of interest, representing a second time. A registration component is configured to register the first image to the second image. A difference component is configured to generate a difference image from the first image and the second image. A pattern recognition component is configured to assign a clinical parameter to the region of interest from the difference image and at least one of the first image and the second image.