The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net. Olaf Ronneberger, et al., "U-Net: Convolutional Networks for Biomedical Image Segmentation" htt…

Segmentation of a 512x512 image takes less than a second on a recent GPU. Olaf Ronneberger, et al., "U-Net: Convolutional Networks for Biomedical Image Segmentation" https://arxiv.org/abs/1505.04597 医療用画像のようにデータ数をたくさん用意…

Moreover, the network is fast. Olaf Ronneberger, et al., "U-Net: Convolutional Networks for Biomedical Image Segmentation" https://arxiv.org/abs/1505.04597 医療用画像のようにデータ数をたくさん用意できない場合においても、精度の良いセグメン…

Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Olaf Ronneberger, et al., "U-Net: Convolutional Networks for…

We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic…

The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. Olaf Ronneberger, et al., "U-Net: Convolutional Networks for Biomedical Image Segmentation" https://arxiv.…

In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. Olaf Ronneberger, et al., "U-Net: Convolutional Networks for Biomedical I…

There is large consent that successful training of deep networks requires many thousand annotated training samples. Olaf Ronneberger, et al., "U-Net: Convolutional Networks for Biomedical Image Segmentation" https://arxiv.org/abs/1505.04597…