U-Net

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The 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.

Network architecture

The network consists of a contracting path and an expansive path, which gives it the u-shaped architecture. The contracting path is a typical convolutional network that consists of repeated application of convolutions, each followed by a rectified linear unit (ReLU) and a max pooling operation. During the contraction, the spatial information is reduced while feature information is increased. The expansive pathway combines the feature and spatial information through a sequence of up-convolutions and concatenations with high-resolution features from the contracting path.^[3]

The basic articles on the system ^{[1] [2]} have been cited 1638 and 1086 times respectively on Google Scholar as of May 19, 2018^[4]

References