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Updated: Jun 12, 2025
Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery
Published on: December 6, 2024
POLYCORE: Polygon-based contour refinement for improved Intravascular Ultrasound Segmentation
Kit Mills Bransby1, Retesh Bajaj2, Anantharaman Ramasamy2
1School of Electronic Engineering and Computer Science, Queen Mary University of London, UK; Digital Environment Research Institute, Queen Mary University of London, UK.
Insights
We developed POLYCORE, a novel method for coronary vessel wall segmentation in intravascular ultrasound. It improves accuracy in challenging regions with artifacts, outperforming existing techniques for better coronary intervention guidance.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiovascular Interventions
Background:
- Accurate segmentation of the coronary vessel wall in intravascular ultrasound (IVUS) is crucial for guiding interventions.
- Existing dense-based neural networks struggle with image artifacts and shadowed regions, leading to anatomically implausible contours.
- Challenges include calcified plaque, guide wires, and side branches that obscure vessel structures.
Purpose of the Study:
- To introduce a novel methodology, Polygon-based Contour Refiner (POLYCORE), to improve coronary vessel wall segmentation.
- To address topological errors and over-smoothing issues in current dense-based and polygon segmentation networks.
- To enhance the learning of anatomically rational contours in challenging IVUS imaging scenarios.
Main Methods:
- Developed POLYCORE, a novel methodology employing a relational inductive bias through higher-order connections between vertices.
- Introduced a vector field refinement module to iteratively add pixel-level detail and remedy over-smoothing.
- Enhanced the approach with augmented polygon aggregation, proving more effective than standard test-time augmentation.
Main Results:
- Achieved state-of-the-art results on two diverse datasets for coronary vessel wall segmentation.
- Demonstrated significant improvements in segmenting the lumen structure.
- Showed particular efficacy in topologically challenging regions affected by shadow artifacts.
Conclusions:
- POLYCORE effectively addresses topological errors and over-smoothing in coronary vessel segmentation.
- The method enhances segmentation accuracy, especially in difficult imaging conditions.
- POLYCORE offers a promising advancement for guiding coronary interventions through improved IVUS analysis.
Abstract:
Segmentation of the coronary vessel wall in intravascular ultrasound is a fundamental step in guiding coronary intervention. However, it is an challenging task, even for highly skilled cardiologists, due to image artefacts and shadowed regions caused by calcified plaque, guide wires and vessel side branches. Recently, dense-based neural networks have been applied to this task, however, they often fail to predict anatomically plausible contours in these low-signal areas. We propose a novel methodology called Polygon-based Contour Refiner (POLYCORE) that addresses topological error in dense-based segmentation networks using a relational inductive bias through higher-order connections between vertices to learn anatomically rational contours. Our approach remedies the over-smoothing phenomena common in polygon networks by introducing a new vector field refinement module which enables pixel-level detail to be added in an iterative process. POLYCORE is enhanced with augmented polygon aggregation which we show is more effective than typical dense-based test-time augmentation strategies. We achieve state-of-the-art results on two diverse datasets, observing particular improvements when segmenting the lumen structure and in topologically-challenging regions containing shadow artefacts. Our source code is available here: https://github.com/kitbransby/POLYCORE.
