Vessel Behavior

Research on vessel behavior encompasses the analysis and modeling of vessel structures and trajectories across various applications, primarily focusing on accurate segmentation and representation for improved diagnosis and prediction. Current efforts leverage deep learning architectures, such as U-Nets and hybrid neural networks, along with advanced algorithms like RRT variants and novel filter fusion methods, to enhance segmentation accuracy, particularly for small vessels, and improve trajectory clustering for behavioral analysis. These advancements have significant implications for medical imaging (e.g., improved diagnosis of cardiovascular diseases), maritime applications (e.g., enhanced vessel traffic management and identification of illegal activities), and computational fluid dynamics (e.g., more accurate blood flow simulations). The development of robust and efficient methods for vessel analysis is crucial for advancing these diverse fields.