Arteriovenous Tree Separation via Vessel Potential Connectivity Map
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Solution Overview
Problem
Current methods for automated segmentation of retinal vessels in fundus photographs are limited in separating arterial from venous trees, often relying on local information and being prone to errors due to imperfections in imaging and vessel extraction, and are generally greedy, leading to potential misclassification and propagation of errors.
Innovation Solution
A framework that generates a vessel potential connectivity map (VPCM) to separate overlapping tree structures into distinct arterial and venous trees by modeling the problem as a graph optimization problem, using a meta-heuristic algorithm to find near-optimal solutions that fit the retinal vasculature topology, and dynamically adjusting vessel segments to resolve connectivity issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If local and greedy segmentation methods are used for vessel separation, then the method is simple to implement, but the accuracy deteriorates due to error propagation and susceptibility to imaging imperfections
Solution Approach 1:
The patent segments the vessel separation problem into distinct phases: initial segmentation using local methods, error identification, and global optimization. This allows the system to benefit from simple local methods while correcting their limitations through subsequent global processing.
Solution Approach 2:
The patent implements feedback mechanisms where segmentation results are evaluated, errors are identified, and corrections are applied iteratively. The system uses feedback from imaging quality assessment to adjust separation parameters and reduce error propagation.
2Measurement precision
If sophisticated global optimization methods are used for vessel separation, then the accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by conducting initial vessel segmentation and identifying potential error regions before applying complex global optimization. This preliminary processing reduces the scope and computational burden of subsequent sophisticated methods.
Solution Approach 2:
The patent applies different levels of processing complexity to different regions: simple methods for clear regions and sophisticated optimization only where needed (at crossing points and ambiguous regions). This localized approach maintains accuracy while reducing overall computational complexity.
3Productivity
If local greedy methods are used for vessel separation, then the processing speed is fast, but reliability deteriorates due to susceptibility to errors in low-contrast images
Solution Approach 1:
The patent applies partial sophisticated processing only to critical regions (vessel crossings and ambiguous areas) rather than processing the entire image with complex methods. This maintains fast processing speed while improving reliability where it matters most.
Solution Approach 2:
The patent introduces intermediary processing steps including quality assessment modules and error detection mechanisms that mediate between fast local methods and reliable global optimization, ensuring speed is maintained while reliability is improved.
4Productivity
If error propagation is allowed in the segmentation process, then the processing is simpler and faster, but the final accuracy deteriorates
Solution Approach 1:
The patent prepares for potential errors by implementing error detection and correction mechanisms in advance. The system cushions against error propagation by continuously monitoring segmentation quality and applying corrections before errors can spread through the processing pipeline.
Data Source
AI summary
Provided are systems and methods for analyzing images. An exemplary method can comprise receiving at least one image having one or more annotations indicating a feature. The method can comprise generating training images from the at least one image. Each training image can be based on a respective section of the at least one image. The training images can comprise positive images having the feature and negative images without the feature. The method can comprise generating a feature space based on the positive images and the negative images. The method can further comprise identifying the feature in one or more unclassified images based upon the feature space.