Cerebral Vascular Bifurcation Detection via Graph Analysis
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Solution Overview
Problem
Current methods for detecting bifurcations in vascular trees, particularly for diagnosing aneurysms, suffer from inadequate precision, which can lead to inaccurate predictions and treatments.
Innovation Solution
A computer-implemented method that processes three-dimensional images of vascular trees to obtain a skeleton, analyzes it to create a graph, and detects bifurcations by identifying nodes linked to at least three edges, calculating bifurcation angles, cross-section areas, and tortuosity using weighted Minkowski sum, thereby improving the accuracy of bifurcation detection and related parameter determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional bifurcation detection methods are used, then the detection process is simple, but the precision of bifurcation detection is inadequate
Solution Approach 1:
The detection process is divided into distinct stages: vessel segmentation from 3D images, skeletonization to extract centerlines, graph construction to represent vascular topology, and bifurcation detection by identifying nodes with degree ≥3. This segmentation allows each stage to be optimized independently, improving overall precision without overwhelming complexity
Solution Approach 2:
The method transitions from 3D volumetric data to a 1D skeleton representation, then to a graph structure with nodes and edges. This dimensional reduction simplifies the detection problem while preserving topological relationships, enabling precise bifurcation identification through graph theory principles
2Measurement precision
If manual measurement methods are used for validation, then the ground truth is accurate, but the time consumption is high
Solution Approach 1:
The method creates a graph-based representation (copy) of the vascular structure that preserves topological information. This graph model serves as an intermediate representation that can be automatically analyzed for bifurcation detection, reducing reliance on time-consuming manual measurements while maintaining accuracy through structured data representation
Data Source
AI summary
Technics are known to use vessel bifurcations detection to obtain access to the detection of aneurysms. However, such technics suffer from a relatively poor accuracy. Therefore, the Applicants have developed a specific method for locating and characterizing bifurcations of a cerebral vascular tree based on a graph analysis of a three-dimensional skeleton of the tree. This enables to determine more accurately the vessel bifurcations. Such property can be used advantageously for several applications such as predicting the risk of developing an aneurysm, diagnosing an aneurysm, identifying a therapeutic target, identifying a biomarker or screening a compound.