U-Net

U-Net: Convolutional networks for biomedical image segmentation. There is large consent that successful training of deep networks requires many thousand annotated training samples. 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. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. 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 stacks. 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. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net


References in zbMATH (referenced in 71 articles )

Showing results 1 to 20 of 71.
Sorted by year (citations)

1 2 3 4 next

  1. Ao, Wenqi; Li, Wenbin; Qian, Jianliang: A data and knowledge driven approach for SPECT using convolutional neural networks and iterative algorithms (2021)
  2. Barrowclough, Oliver J. D.; Muntingh, Georg; Nainamalai, Varatharajan; Stangeby, Ivar: Binary segmentation of medical images using implicit spline representations and deep learning (2021)
  3. Chen, Li-Wei; Cakal, Berkay A.; Hu, Xiangyu; Thuerey, Nils: Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates (2021)
  4. Davy, Axel; Ehret, Thibaud; Morel, Jean-Michel; Arias, Pablo; Facciolo, Gabriele: Video denoising by combining patch search and CNNs (2021)
  5. Haiping Lu, Xianyuan Liu, Robert Turner, Peizhen Bai, Raivo E Koot, Shuo Zhou, Mustafa Chasmai, Lawrence Schobs: PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python (2021) arXiv
  6. He, Fang; Chun, Rachel Ka Man; Qiu, Zicheng; Yu, Shijie; Shi, Yun; To, Chi Ho; Chen, Xiaojun: Choroid segmentation of retinal OCT images based on CNN classifier and (l_2-l_q) fitter (2021)
  7. Ivek, Tomislav; Vlah, Domagoj: BlackBox: generalizable reconstruction of extremal values from incomplete spatio-temporal data (2021)
  8. Jia, Fan; Liu, Jun; Tai, Xue-Cheng: A regularized convolutional neural network for semantic image segmentation (2021)
  9. Khatri, Rajendra K. C.; Caseria, Brendan J.; Lou, Yifei; Xiao, Guanghua; Cao, Yan: Automatic extraction of cell nuclei using dilated convolutional network (2021)
  10. Kowal, Marek; Skobel, Marcin; Gramacki, Artur; Korbicz, Józef: Breast cancer nuclei segmentation and classification based on a deep learning approach (2021)
  11. Mingxiang Chen, Zhanguo Chang, Haonan Lu, Bitao Yang, Zhuang Li, Liufang Guo, Zhecheng Wang: AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation (2021) arXiv
  12. Tang, Meng; Liu, Yimin; Durlofsky, Louis J.: Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow (2021)
  13. Yang, Hongfei; Ding, Xiaofeng; Chan, Raymond; Hu, Hui; Peng, Yaxin; Zeng, Tieyong: A new initialization method based on normed statistical spaces in deep networks (2021)
  14. Zhang, Chengkai; Song, Xianzhi; Azevedo, Leonardo: U-net generative adversarial network for subsurface facies modeling (2021)
  15. Arridge, S.; Hauptmann, A.: Networks for nonlinear diffusion problems in imaging (2020)
  16. Baguer, Daniel Otero; Leuschner, Johannes; Schmidt, Maximilian: Computed tomography reconstruction using deep image prior and learned reconstruction methods (2020)
  17. Breger, A.; Orlando, J. I.; Harar, P.; Dörfler, M.; Klimscha, S.; Grechenig, C.; Gerendas, B. S.; Schmidt-Erfurth, U.; Ehler, M.: On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems (2020)
  18. Bullock, Joseph; Luccioni, Alexandra; Pham, Katherine Hoffman; Lam, Cynthia Sin Nga; Luengo-Oroz, Miguel: Mapping the landscape of artificial intelligence applications against COVID-19 (2020)
  19. Charley Gros, Andreanne Lemay, Olivier Vincent, Lucas Rouhier, Anthime Bucquet, Joseph Paul Cohen, Julien Cohen-Adad: ivadomed: A Medical Imaging Deep Learning Toolbox (2020) arXiv
  20. Cheng, Yinlin; Ma, Mengnan; Zhang, Liangjun; Jin, ChenJin; Ma, Li; Zhou, Yi: Retinal blood vessel segmentation based on densely connected U-net (2020)

1 2 3 4 next