Vessel Segmentation

Vessel segmentation, the automated identification of blood vessels in medical images, aims to improve diagnostic accuracy and streamline clinical workflows. Current research heavily utilizes deep learning, particularly U-Net and transformer-based architectures, often incorporating shape priors, multi-task learning, and contrastive learning strategies to enhance segmentation accuracy, especially for small or poorly defined vessels. This work is crucial for various applications, including surgical planning, disease diagnosis (e.g., coronary artery disease, cerebrovascular diseases), and treatment monitoring, ultimately improving patient care and accelerating medical research. Challenges remain in handling image noise, variability across modalities and patients, and the need for large, high-quality annotated datasets.