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

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  1. Cheng, Lin; Wagner, Gregory J.: A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization (2022)
  2. Gribonval, RĂ©mi; Kutyniok, Gitta; Nielsen, Morten; Voigtlaender, Felix: Approximation spaces of deep neural networks (2022)
  3. Ma, Chao; Shen, Lijun; Deng, Hao; Li, Jialin: Synaptic clef segmentation method based on fractal dimension for ATUM-SEM image of mouse cortex (2022)
  4. Ma, Qianting; Zeng, Tieyong; Kong, Dexing; Zhang, Jianwei: Weighted area constraints-based breast lesion segmentation in ultrasound image analysis (2022)
  5. Ma, Tian; Hessenkemper, Hendrik; Lucas, Dirk; Bragg, Andrew D.: An experimental study on the multiscale properties of turbulence in bubble-laden flows (2022)
  6. Wang, Hengjie; Planas, Robert; Chandramowlishwaran, Aparna; Bostanabad, Ramin: Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (2022)
  7. Adewoyin, Rilwan A.; Dueben, Peter; Watson, Peter; He, Yulan; Dutta, Ritabrata: TRU-NET: a deep learning approach to high resolution prediction of rainfall (2021)
  8. Ao, Wenqi; Li, Wenbin; Qian, Jianliang: A data and knowledge driven approach for SPECT using convolutional neural networks and iterative algorithms (2021)
  9. Barrowclough, Oliver J. D.; Muntingh, Georg; Nainamalai, Varatharajan; Stangeby, Ivar: Binary segmentation of medical images using implicit spline representations and deep learning (2021)
  10. Chen, Guodong; Fidkowski, Krzysztof J.: Output-based adaptive aerodynamic simulations using convolutional neural networks (2021)
  11. Chen, Li-Wei; Cakal, Berkay A.; Hu, Xiangyu; Thuerey, Nils: Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates (2021)
  12. Davy, Axel; Ehret, Thibaud; Morel, Jean-Michel; Arias, Pablo; Facciolo, Gabriele: Video denoising by combining patch search and CNNs (2021)
  13. de Hoop, Maarten V.; Lassas, Matti; Wong, Christopher A.: Deep learning architectures for nonlinear operator functions and nonlinear inverse problems (2021)
  14. 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
  15. He, Chunmei; Wang, Shunmin; Kang, Hongyu; Zheng, Lanqing; Tan, Taifeng; Fan, Xianjun: Adversarial domain adaptation network for tumor image diagnosis (2021)
  16. Ivek, Tomislav; Vlah, Domagoj: BlackBox: generalizable reconstruction of extremal values from incomplete spatio-temporal data (2021)
  17. Jia, Fan; Liu, Jun; Tai, Xue-Cheng: A regularized convolutional neural network for semantic image segmentation (2021)
  18. Kaushal, C.; Kaushal, Kirti; Singla, A.: Firefly optimization-based segmentation technique to analyse medical images of breast cancer (2021)
  19. Khatri, Rajendra K. C.; Caseria, Brendan J.; Lou, Yifei; Xiao, Guanghua; Cao, Yan: Automatic extraction of cell nuclei using dilated convolutional network (2021)
  20. Koo, Bongyeong; Choi, Han-Soo; Kang, Myungjoo: Simple feature pyramid network for weakly supervised object localization using multi-scale information (2021)

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