Vascular Tree Segmentation Using ML Image Selection
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Solution Overview
Problem
Current vascular imaging techniques suffer from low contrast and complex environments, leading to error-prone vascular segmentation and feature identification, especially in arterial stenosis assessment, which often requires invasive procedures.
Innovation Solution
A system utilizing machine learning models, such as convolutional neural networks, to select an optimal angiographic image from a sequence based on cardiac phase and image quality, enhancing vascular image contrast and reducing errors by analyzing segmentation masks for size and clarity scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated vascular segmentation is performed, then time and effort required for manual identification is reduced, but the output becomes error-prone due to low contrast and complex environment
Solution Approach 1:
The patent applies segmentation by dividing the vascular image processing into distinct stages: initial automated segmentation to identify potential vascular regions, followed by secondary verification steps to confirm and refine the segmentation results. This multi-stage segmentation approach maintains high productivity while improving reliability by catching errors at different processing stages.
Solution Approach 2:
The patent implements feedback mechanisms where segmentation results are continuously evaluated and refined. The system provides feedback loops that allow automated correction of segmentation errors, with the ability to iteratively improve segmentation accuracy by analyzing contrast variations and environmental complexity in different vascular regions.
2Measurement precision
If manual vascular identification is performed, then segmentation accuracy is improved, but time and skill requirements increase significantly
Solution Approach 1:
The patent applies preliminary action by performing automated pre-segmentation and feature detection before manual review. This prepares the data in advance by identifying likely vascular regions and characteristics, so that when manual verification is needed, the operator starts with pre-processed information rather than raw images, significantly reducing the time and skill burden while maintaining high accuracy.
3Illumination intensity
If image contrast is low, then vascular features are difficult to distinguish, but increasing contrast may amplify noise and extraneous features
Solution Approach 1:
The patent applies local quality by implementing contrast enhancement that is spatially adaptive - different regions of the image receive different contrast adjustments based on local characteristics. Vascular regions receive enhanced contrast to improve visibility, while regions prone to noise amplification receive more conservative processing. This localized approach maintains vascular feature distinguishability without uniformly amplifying noise across the entire image.
Data Source
AI summary
Methods for automated identification of vascular features are described. In some embodiments, one or more machine learning (ML)-based vascular classifiers are used, with their results being combined to with results of at least one other vascular classifier in order to produce the final results. Potentially advantages of this approach include the ability to combine certain strengths of ML classifiers with segmentation approaches based on more classical (“formula-based”) methods. These strengths may include particularly the identification of anatomically identified targets mixed within an image also showing similar looking but anatomically distinct targets.