Blood vessel segmentation is a crucial aspect of medical image processing, aiding medical professionals in more accurate disease analysis and diagnosis. Manual blood vessel segmentation methods are time-consuming and cumbersome, making the development of automatic segmentation methods essential. The rapid advancements in deep learning technology have introduced new tools and methods for vascular image segmentation. In this review, we provide a comprehensive overview of deep learning-based blood vessel segmentation methods across various fields, including retinal vessel segmentation, cerebrovascular segmentation, and pulmonary vessel segmentation. Several prevalent diseases, such as retinal vascular diseases, cerebrovascular diseases, pulmonary vascular diseases, and tumors, have posed significant health challenges globally. This review also discusses the application of deep learning technology in disease diagnosis within these contexts. Finally, considering the current research landscape, we discuss existing challenges and potential future developments in blood vessel segmentation. We aim to assist researchers in gaining a comprehensive understanding and designing effective blood vessel segmentation models, ultimately offering opportunities for early disease diagnosis and treatment.

Reactive oxygen species (ROS) play pivotal roles in diverse physiological processes, exerting a significant influence on various organ systems within the human body. Recently, there has been a notable upswing in the design of nanomaterials based on natural enzymes with the ability to scavenge ROS. These nanomaterials hold promise as potent alternatives to conventional antioxidants. However, the conventional design of these materials has often relied on empirical and trial-and-error methods, posing challenges in capturing the intricate conformational relationships of nanozymes. This comprehensive review aims to consolidate rational design strategies and applications of nanozymes. Primarily, it advocates for an in-depth exploration of nanozyme mechanisms to facilitate precise design. Four rational design strategies: biomimetic design, experimental laws-driven design, computation-driven design, and data-driven design are scrutinized while considering their respective advantages, disadvantages, and application conditions. The review subsequently delves into the diverse applications of nanozymes across fields such as inflammatory diseases treatment, disease diagnosis, and environmental applications. Finally, the review outlines the challenges and prospects associated with the rational design of nanozymes while providing a comprehensive overview of this burgeoning area of research.