ML Vascular Segmentation Through Optimal Image Selection
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Solution Overview
Problem
Existing vascular imaging techniques suffer from low contrast and complex environments, leading to error-prone vascular segmentation and feature identification, which can result in inaccurate three-dimensional models of arterial stenosis.
Innovation Solution
A system utilizing machine learning models, such as convolutional neural networks, to select an optimal angiographic image from a sequence, enhancing contrast and reducing errors by analyzing segmentation masks for size and clarity scores, thereby improving the accuracy of downstream processes like three-dimensional model generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automated vascular segmentation is performed using conventional methods, then time and effort are reduced, but measurement precision deteriorates due to low contrast and complex environment
Solution Approach 1:
The patent introduces an intermediary optimization process that selects optimal input images from a sequence based on contrast and clarity metrics before they undergo segmentation. This intermediary step filters out suboptimal images that would compromise measurement precision, while the automated segmentation still provides time efficiency. The intermediary acts as a gatekeeper that ensures only high-quality images proceed to the segmentation pipeline.
Solution Approach 2:
The patent performs preliminary optimization of image selection before the main segmentation task. By pre-evaluating images based on contrast and clarity scores and selecting only the optimal ones, the system prepares the best possible input data in advance. This preliminary action ensures that the subsequent automated segmentation operates on high-quality data, thereby maintaining measurement precision while still benefiting from automation.
2Object-affected harmful factors
If conventional vascular imaging is used, then radiation exposure is limited, but manufacturing precision of three-dimensional models deteriorates due to error-prone segmentation
Solution Approach 1:
The patent introduces an intermediary optimization process that selects optimal input images from a sequence based on contrast and clarity metrics before they undergo segmentation. This intermediary step filters out suboptimal images that would compromise measurement precision, while the automated segmentation still provides time efficiency. The intermediary acts as a gatekeeper that ensures only high-quality images proceed to the segmentation pipeline.
Solution Approach 2:
The patent replaces manual vascular segmentation and feature identification with an automated machine learning-based system. This substitution eliminates human error while maintaining high precision through algorithms that evaluate contrast and clarity metrics. The automated system achieves manufacturing precision comparable to or exceeding manual methods without requiring additional radiation exposure for multiple imaging attempts.
3Measurement precision
If manual vascular segmentation is performed, then measurement precision is maintained, but ease of operation deteriorates due to high skill requirement and time consumption
Solution Approach 1:
The patent implements a self-service automated segmentation system that performs vascular feature identification without requiring skilled operators. The machine learning algorithms autonomously evaluate images, select optimal inputs based on contrast and clarity, and generate segmentation results. This self-service capability maintains measurement precision while dramatically improving ease of operation by eliminating the need for skilled manual intervention.
Solution Approach 2:
The patent replaces manual vascular segmentation and feature identification with an automated machine learning-based system. This substitution eliminates human error while maintaining high precision through algorithms that evaluate contrast and clarity metrics. The automated system achieves manufacturing precision comparable to or exceeding manual methods without requiring additional radiation exposure for multiple imaging attempts.
4Measurement precision
If multiple images are analyzed to improve segmentation accuracy, then measurement precision is improved, but loss of time increases due to processing more data
Solution Approach 1:
The patent performs preliminary optimization of image selection before the main segmentation task. By pre-evaluating images based on contrast and clarity scores and selecting only the optimal ones, the system prepares the best possible input data in advance. This preliminary action ensures that the subsequent automated segmentation operates on high-quality data, thereby maintaining measurement precision while still benefiting from automation.
Solution Approach 2:
The patent extracts and selects only the most relevant and high-quality images from a sequence for segmentation analysis. By taking out and focusing on only the optimal images based on contrast and clarity metrics, the system avoids processing unnecessary data, thereby maintaining measurement precision while reducing the time loss associated with analyzing multiple suboptimal images.
Data Source
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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.