POLYCORE: Polygon-based contour refinement for improved Intravascular Ultrasound Segmentation.

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.