Vascular Image Segmentation Using Centerline-Guided Lumen and Wall Detection
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Solution Overview
Problem
Current methods for identifying and analyzing blood vessels in vascular images are cumbersome, inefficient, and lack accuracy, particularly in determining vascular centerlines and segmenting the lumen and wall of blood vessels, which are crucial for diagnosing cerebrovascular diseases.
Innovation Solution
The use of machine learning models to automatically determine the centerline and boundaries of blood vessels, segment images, and analyze vascular parameters, including the application of multiple imaging sequences to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to identify blood vessels and determine centerlines, then the process can be performed with simple equipment, but the analysis is cumbersome, inefficient, and lacks accuracy
Solution Approach 1:
The patent replaces manual mechanical analysis methods with an automated computer-based image processing system. The system uses algorithms to automatically determine blood vessel centerlines, segment lumen and wall regions, and extract vascular parameters from medical images, eliminating the need for manual tracing and analysis while significantly improving accuracy and efficiency
Solution Approach 2:
The patent introduces an intermediary image processing system that acts as a mediator between the raw medical images and the final vascular analysis. This intermediate system performs automated centerline determination, segmentation, and parameter extraction, serving as a bridge that transforms complex image data into actionable medical insights without requiring direct manual interpretation
2Productivity
If automated image processing is implemented to improve efficiency, then the analysis speed increases, but the complexity of the processing system increases
Solution Approach 1:
The patent segments the vascular image analysis process into distinct functional modules: centerline determination, lumen segmentation, wall segmentation, and parameter extraction. Each module handles a specific aspect of the analysis independently, which improves overall efficiency while allowing the system complexity to be managed through modular architecture
Solution Approach 2:
The automated image processing system performs self-service by automatically determining centerlines, segmenting vascular structures, and extracting parameters without requiring manual intervention. The system processes images independently and generates analysis results autonomously, significantly improving productivity while the complexity is encapsulated within the automated algorithms
3Measurement precision
If multiple imaging sequences are applied to enhance accuracy, then the measurement precision improves, but the time required for image acquisition increases
Solution Approach 1:
The patent performs preliminary actions by automatically determining centerlines and segmenting vascular structures from the acquired images before final parameter measurement. This preliminary processing prepares the image data in advance, allowing the system to efficiently extract accurate vascular parameters without requiring additional time-consuming manual measurement procedures
Data Source
AI summary
Systems and methods for image processing are provided. The systems may include obtaining an initial image relating to a blood vessel. The system may include determining a centerline of the blood vessel based on the initial image. The system may also include determining one or more images to be segmented of the blood vessel based on the centerline and the initial image. The system may also include determining a boundary of the lumen of the blood vessel and a boundary of the wall of the blood vessel in the each image for each of the one or more images. The system may further include analyzing the blood vessel based on the one or more boundaries of the lumen and the one or more boundaries of the wall.