Cerebrovascular Segmentation

Cerebrovascular segmentation, the automated identification of blood vessels in medical images like MRAs and CTAs, aims to improve the diagnosis and treatment of cerebrovascular diseases. Current research focuses on addressing challenges like inconsistent annotations and limited labeled data through semi-supervised learning, synthetic data augmentation, and novel network architectures such as UNets and Transformers, often incorporating techniques like attention mechanisms and Frangi filtering for improved accuracy. These advancements hold significant potential for accelerating clinical workflows, enabling more precise diagnoses, and facilitating personalized treatment planning for conditions such as aneurysms and strokes.