Automated Head-Neck Artery Segmentation in CT Images
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Solution Overview
Problem
Current medical image segmentation tools require significant manual input and are impractical for routine segmentation of large numbers of medical images, particularly for CT images of head-neck arteries, due to challenges such as non-uniform CT numbers, unclear vessel edges, and noise.
Innovation Solution
A fully automated system and method for segmenting head-neck arteries, brain, and skull in CT images, which involves identifying circular components in parallel planes, defining contiguous segments, and using anatomical landmarks to classify and segment blood vessels, allowing for semi-automated seed identification when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation methods are used, then segmentation accuracy can be maintained, but the time required and operator workload increase significantly
Solution Approach 1:
The patent segments the blood vessel extraction process into distinct phases: initial extraction using contrast agent enhancement, refinement through morphological operations, and final validation. This multi-stage automated segmentation approach maintains accuracy while eliminating manual intervention time.
Solution Approach 2:
The system performs preliminary actions by automatically identifying contrast agent distribution patterns and pre-segmenting potential vessel regions before final refinement. This preliminary automated work reduces the need for manual correction and accelerates the overall process.
2Productivity
If automated segmentation algorithms are implemented, then processing speed increases, but reliability and accuracy may deteriorate due to image quality issues
Solution Approach 1:
The patent incorporates feedback mechanisms where the segmentation results are continuously evaluated against image quality metrics. The system adjusts processing parameters based on detected image conditions (noise levels, contrast distribution), ensuring reliable results across varying image qualities while maintaining automated processing speed.
Solution Approach 2:
The system dynamically changes processing parameters based on image characteristics. For example, threshold values, filtering strengths, and morphological operation parameters are adjusted according to the specific image's noise level, contrast distribution, and vessel visibility, maintaining reliability across diverse image qualities.
3Difficulty of detecting and measuring
If contrast agents are used to enhance vessel visibility, then vessel detection improves, but non-uniform distribution and artifacts may reduce measurement precision
Solution Approach 1:
The patent extracts and removes the contrast agent enhancement effect from the final measurement calculation. The system identifies contrast-enhanced regions, separates the anatomical vessel structure from the contrast artifact, and performs measurements on the extracted vessel geometry, eliminating contrast distribution non-uniformity from measurement errors.
Solution Approach 2:
The system introduces intermediate processing steps including morphological operations and skeletonization that act as mediators between the contrast-enhanced image and the final measurement. These intermediate representations filter out contrast-related artifacts while preserving the underlying vessel geometry for accurate measurement.
4Ease of operation
If skull and brain tissue are removed from the image, then vessel visualization improves, but additional segmentation steps increase processing complexity
Solution Approach 1:
The patent merges the skull/brain segmentation process with the vessel segmentation process into a unified automated workflow. Both segmentation tasks share common preprocessing steps and are coordinated through a single control system, reducing overall system complexity despite the multiple segmentation operations required.
Data Source
AI summary
A method for automated segmentation of a blood vessel of a head and neck of a subject in a medical image, the method comprising: identifying the location of anatomical landmarks in the medical image; identifying regions of interest in the medical image based on the landmarks; segmenting segments of blood vessels in the medical image; classifying at least one of the segments as defining the blood vessel based on its position relative to the landmarks within the regions of interest to create a classified blood vessel; identifying a starting seed for the blood vessel from the classified blood vessel; identifying an ending seed for the blood vessel from the classified blood vessel; segmenting the blood vessel between the starting seed and the ending seed; and defining a path between the starting seed and the ending seed.