Vessel Segmentation Using Adaptive Thresholds for Boundary Accuracy
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Solution Overview
Problem
Current medical imaging techniques for blood vessel segmentation, such as CT and MRI, face challenges in accurately determining vessel boundaries due to variations in contrast medium intensity over time, distance, and vessel size, leading to inaccuracies in 3D reconstruction and visualization.
Innovation Solution
A vessel segmentation method that sets multiple threshold values based on data from a database, considering intensity characteristics and physiological factors, to accurately determine vessel boundaries by adjusting threshold values according to the intensity of the contrast medium in each cross-section, thereby improving segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single threshold value is used for vessel segmentation, then the segmentation process is simple and fast, but the accuracy of vessel boundary determination deteriorates due to variations in contrast medium intensity
Solution Approach 1:
The patent applies local quality by setting different threshold values for different cross-sections of blood vessels based on their specific contrast medium intensity characteristics. Each cross-section receives a customized threshold value tailored to its local intensity profile, thereby improving vessel boundary determination accuracy while accounting for spatial variations in contrast enhancement.
Solution Approach 2:
The patent implements dynamics by making threshold values adaptive rather than static. The threshold for each cross-section is dynamically determined based on the measured contrast medium intensity at that location, allowing the segmentation process to adapt to varying intensity conditions throughout the vascular structure.
2Measurement precision
If multiple threshold values are set based on contrast medium intensity data, then vessel segmentation accuracy is improved, but the processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-acquiring and storing contrast medium intensity data in a database before performing vessel segmentation. This preparatory step allows the system to quickly retrieve appropriate threshold values during segmentation without performing complex calculations in real-time, thereby reducing processing time while maintaining high accuracy.
Solution Approach 2:
The patent uses copying by creating a lookup table or database of pre-computed threshold values based on contrast medium intensity characteristics. During segmentation, the system copies the appropriate threshold value from this database rather than recalculating it, significantly reducing computational overhead and processing time.
3Measurement precision
If higher contrast medium intensity is used to improve vessel visualization, then segmentation accuracy improves, but the amount of contrast medium required increases leading to greater adverse effects
Solution Approach 1:
The patent applies parameter changes by shifting the optimization parameter from contrast medium intensity to threshold value selection. Instead of increasing contrast medium dosage to improve visualization, the system adjusts the threshold parameter based on the actual contrast intensity present, achieving high segmentation accuracy with lower contrast medium amounts and reduced adverse effects.
Data Source
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AI summary
A vessel segmentation method includes acquiring an image of a blood vessel, including cross sections, using a contrast medium. The method further includes setting a threshold value for each of the cross sections based on data of an intensity of the contrast medium. The method further includes performing vessel segmentation based on the image and the threshold value for each of the cross sections.