Progress in vessel tortuosity evaluation algorithm research
Vessel tortuosity,referring to the degree of twisting and turning of vessels in space,is closely associated with various diseases, including Alzheimer disease, atherosclerosis, and others, serving as a crucial indicator for disease diagnosis. Consequently,the accurate evaluation of vessel tortuosity is of significant value for clinical diagnosis,treatment,and prognosis. With the advancement of medical imaging technology,research on the evaluation of vessel tortuosity has made remarkable progress. This article provides a comprehensive review of the primary methods and technologies for evaluating vessel tortuosity and evaluates their respective strengths and weaknesses. The discussion encompasses algorithms based on distance,angle,curvature,and deep learning for evaluating vessel tortuosity. Distance-based algorithms are noted for their simplicity and intuitiveness. In contrast, angle and curvature-based algorithms, which evaluate tortuosity by analyzing changes in angle and curvature along the vessel ' s centerline, excel in capturing local features. Moreover, the emergence of deep learning algorithms in recent years,characterized by their ability to automatically learn and recognize complex vessel structures,has enriched the field. These algorithms contribute to the accurate and stable evaluation of vessel tortuosity, offering effective tools for clinical practice. However, current research is not without its limitations,with issues related to the universality of algorithms, individual variations, and real-time processing yet to be addressed. The ongoing exploration and refinement of vessel tortuosity evaluation techniques and algorithms to better meet clinical needs and enhance the precision and efficiency of disease diagnosis are essential for advancing the field. This article aims to guide future research, offering valuable insights and references for the continued development of vessel tortuosity evaluation studies.
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维普
