UNet Architecture

The UNet architecture is a convolutional neural network primarily used for image segmentation, aiming to accurately delineate objects or regions of interest within images. Current research focuses on improving UNet's performance and efficiency through variations like the Triple-UNet (incorporating multiple UNets for enhanced segmentation) and IP-UNet (reducing computational cost for 3D data). These advancements are significantly impacting biomedical image analysis, enabling more accurate and efficient segmentation in applications such as skin lesion detection, cancer diagnosis from PET/CT scans, and automated analysis of medical volumes.