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Published on: January 18, 2021
A 2D driven 3D vessel segmentation algorithm for 3D digital subtraction angiography data
M Spiegel1, T Redel, T Struffert
1Pattern Recognition Lab, University Erlangen-Nuremberg, Erlangen, Germany. martin.spiegel@informatik.uni-erlangen.de
Insights
A new framework uses 2D digital subtraction angiography (DSA) to improve 3D vessel segmentation accuracy for cerebrovascular disease. This method enhances precision and Dice coefficient, aiding clinical decisions and computational fluid dynamic modeling.
Area of Science:
- Medical Imaging
- Computer-Aided Diagnosis
- Vascular Surgery
Background:
- Cerebrovascular disease is a leading cause of death, necessitating accurate vessel analysis.
- 3D rotational angiography provides crucial vessel morphology and pathology data.
- Current 3D segmentation methods lack patient-specific validation, hindering clinical decision-making.
Purpose of the Study:
- To introduce a novel 2D digital subtraction angiography (DSA)-driven framework for 3D vessel segmentation and validation.
- To enhance the accuracy of 3D vessel segmentation for individual patient analysis.
- To provide a reliable method for validating 3D vessel models against clinical gold standards.
Main Methods:
- Developed a 2D DSA-driven 3D vessel segmentation and validation framework.
- Utilized an ellipsoid vessel model for initial 3D segmentation.
- Iteratively overlaid forward projections of the 3D model with 2D DSA projections for accuracy assessment.
- Employed a global 2D/3D optimization function to refine 3D segmentation based on 2D vessel contours.
Main Results:
- The novel framework demonstrated superior performance compared to state-of-the-art methods like region growing.
- Achieved an improvement of 7.2% in precision and 5.8% in Dice coefficient.
- Successfully evaluated on phantom data and ten patient datasets using three 2D DSA projections per dataset.
Conclusions:
- The proposed 2D DSA-driven 3D vessel segmentation framework offers enhanced accuracy and validation capabilities.
- This method addresses the limitations of existing segmentation techniques by enabling patient-specific validation.
- Paves the way for advanced clinical applications, including computational fluid dynamic modeling, requiring high vessel accuracy.
Abstract:
Cerebrovascular disease is among the leading causes of death in western industrial nations. 3D rotational angiography delivers indispensable information on vessel morphology and pathology. Physicians make use of this to analyze vessel geometry in detail, i.e. vessel diameters, location and size of aneurysms, to come up with a clinical decision. 3D segmentation is a crucial step in this pipeline. Although a lot of different methods are available nowadays, all of them lack a method to validate the results for the individual patient. Therefore, we propose a novel 2D digital subtraction angiography (DSA)-driven 3D vessel segmentation and validation framework. 2D DSA projections are clinically considered as gold standard when it comes to measurements of vessel diameter or the neck size of aneurysms. An ellipsoid vessel model is applied to deliver the initial 3D segmentation. To assess the accuracy of the 3D vessel segmentation, its forward projections are iteratively overlaid with the corresponding 2D DSA projections. Local vessel discrepancies are modeled by a global 2D/3D optimization function to adjust the 3D vessel segmentation toward the 2D vessel contours. Our framework has been evaluated on phantom data as well as on ten patient datasets. Three 2D DSA projections from varying viewing angles have been used for each dataset. The novel 2D driven 3D vessel segmentation approach shows superior results against state-of-the-art segmentations like region growing, i.e. an improvement of 7.2% points in precision and 5.8% points for the Dice coefficient. This method opens up future clinical applications requiring the greatest vessel accuracy, e.g. computational fluid dynamic modeling.
