The article explores the U-Net architecture, a convolutional neural network model designed for image segmentation, particularly in biomedical contexts. Developed by Olaf Ronneberger and colleagues in 2015, U-Net is distinguished by its U-shaped structure, featuring a contracting path and an expansive path, which enhances its ability to perform pixel-based segmentation even with limited datasets. Unlike traditional autoencoders, U-Net effectively overcomes bottleneck issues by connecting the encoder and decoder parts, improving feature retention. The architecture is especially adept at handling complex images, such as those with varying tissue deformations, and employs techniques like elastic deformation to expand training datasets. Its applications extend beyond medicine, benefiting fields such as remote sensing, urban planning, and seismic imaging, where accurate segmentation is crucial. The article also highlights the challenges in data labeling and the importance of sophisticated loss functions like Dice loss for evaluating performance in segmentation tasks.