U-Net: Convolutional Networks for Biomedical Image Segmentation
The paper introduces U-Net, an encoder-decoder convolutional network with a contracting path to capture context and a symmetric expanding path with skip connections for precise localization. Combined with heavy data augmentation, the architecture trains end-to-end from very few annotated images. It won the ISBI cell-tracking and neuronal-structure segmentation challenges and segments a 512x512 image in under a second on a GPU.