U-Net - Wikipedia

U-Net is a convolutional neural network that was developed for biomedical image segmentation at the Computer Science Department of the University of Freiburg, Germany.[1] The network is based on the fully convolutional network[2] and its architecture was modified and extended to work with fewer training images and to yield more precise segmentations. Segmentation of a 512 512 image takes less than a second on a modern GPU. The U-Net architecture stems from the so-called "fully convolutional network" first proposed by Long and Shelhamer.[2] The main idea is to supplement a usual contracting network by successive layers, where pooling operations are replaced by upsampling operators.