Blood Vessel Segmentation

Blood vessel segmentation, the automated identification of blood vessels in medical images, aims to improve diagnostic accuracy and reduce manual workload in various clinical applications. Current research emphasizes developing robust and efficient deep learning models, including convolutional neural networks and generative adversarial networks, often incorporating techniques like contrastive learning and skeletonization algorithms to enhance accuracy and address challenges such as limited labeled data and image variability. These advancements are crucial for improving the diagnosis and treatment of vascular diseases, particularly in areas like ophthalmology and interventional radiology, where precise vessel segmentation is essential.