Segmenting Calcified Blood Vessels via Intensity Profile Correction
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Solution Overview
Problem
Current methods for segmenting calcified blood vessels from image data, such as in coronary CT Angiography, face challenges due to the similarity in contrast between calcified plaques and vessel lumens, leading to difficulties in distinguishing between them, especially in contrast-enhanced image acquisition processes.
Innovation Solution
A method and system that provide a vesseltree representation, preliminary boundary representations, and intensity profiles to detect calcifications, correct boundary representations, and use a Markov Random Field with maximum a posteriori estimation for optimal segmentation, excluding calcifications from the vessel lumen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If contrast-enhanced image acquisition is used to improve vessel lumen visibility, then the vessel lumen becomes more visible, but calcified plaques become indistinguishable from the lumen due to similar contrast appearance
Solution Approach 1:
The patent segments the blood vessel analysis into multiple independent parts: (1) extracting vessel tree representation and cross-sectional boundary representations, (2) detecting calcifications based on intensity profiles, and (3) correcting boundary representations to exclude calcifications. This segmentation allows each step to address specific challenges independently, resolving the contradiction between lumen visibility and calcified plaque distinction.
Solution Approach 2:
The patent extracts calcified plaques from the vessel lumen by identifying regions with intensity values above a threshold in the intensity profiles. By separating the calcification detection step from the overall segmentation process and removing calcified regions from the lumen boundary representation, the method resolves the indistinguishability problem caused by similar contrast appearance.
2Productivity
If automatic detection methods are used to improve diagnostic efficiency, then diagnostic speed increases, but segmentation accuracy deteriorates due to image quality variations and small vessel sizes
Solution Approach 1:
The patent applies local quality by using intensity profiles specific to each cross-section of the blood vessel. Instead of applying a uniform detection threshold across the entire image, the method analyzes intensity variations locally at each cross-sectional position, allowing accurate calcification detection even in small distal vessel parts where image quality varies.
Solution Approach 2:
The patent transitions from analyzing only the spatial dimension of the blood vessel to incorporating the intensity dimension by extracting intensity profiles along the vessel tree. This additional dimensional information enables automatic detection methods to distinguish calcified plaques from the lumen based on intensity characteristics, maintaining high segmentation accuracy while improving diagnostic efficiency.
3Measurement precision
If invasive angiography is used to improve diagnostic accuracy, then diagnostic precision increases, but patient risk and cost increase
Solution Approach 1:
The patent creates a detailed computational model (copy) of the blood vessel lumen by segmenting the CCTA image data and correcting boundary representations. This virtual model allows accurate measurement and analysis of vessel characteristics without requiring invasive procedures, achieving diagnostic precision comparable to invasive angiography while eliminating patient risk and reduced cost.
Solution Approach 2:
The patent replaces the mechanical invasive angiography procedure with a computational image analysis system. By using automated segmentation methods, intensity profile analysis, and boundary correction algorithms, the system achieves accurate diagnostic results through non-invasive CCTA imaging, substituting mechanical intervention with computational processing to eliminate patient risk.
Data Source
AI summary
A method is disclosed for segmentation of a calcified blood vessel in image data. An embodiment of the method includes providing a vesseltree representation of the blood vessel; providing a number of preliminary boundary representations of a number of cross-sections of the blood vessel; providing a number of intensity profiles in the image data in the number of cross-sections; determining a calcification in the cross-section based on the intensity profile; and correcting each preliminary boundary representation into a corrected boundary representation which excludes the calcification from an inner part of the blood vessel. A segmentation system is also disclosed.