Vein Segmentation

Vein segmentation, the automated identification of veins in medical images, aims to improve diagnostic accuracy and efficiency across various applications. Current research heavily utilizes deep learning, employing architectures like U-Net and novel recursive refinement networks, to achieve accurate segmentation in diverse imaging modalities (e.g., retinal fundus images, near-infrared scans, and CT scans). This work is driven by the need for less invasive and more efficient diagnostic tools, impacting fields ranging from ophthalmology and cardiovascular health to biometric authentication and surgical planning. Improved segmentation accuracy leads to better clinical decision-making and a deeper understanding of vascular anatomy and physiology.