Vascular Segmentation

Vascular segmentation, the automated identification and delineation of blood vessels in medical images, aims to improve diagnostic accuracy and treatment planning across various medical specialties. Current research focuses on enhancing the robustness and generalization of deep learning models, particularly U-Net and its variants, often incorporating techniques like attention mechanisms, deformable convolutions, and novel loss functions (e.g., centerline boundary Dice loss) to address challenges posed by image variability and limited training data. These advancements are crucial for applications ranging from diagnosing cardiovascular disease and ophthalmic conditions to guiding minimally invasive surgical procedures, ultimately improving patient care and accelerating medical research.