3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

  • Özgün ÇiçekEmail author
  • Ahmed Abdulkadir
  • Soeren S. Lienkamp
  • Thomas Brox
  • Olaf Ronneberger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)


This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automated setup, the user annotates some slices in the volume to be segmented. The network learns from these sparse annotations and provides a dense 3D segmentation. (2) In a fully-automated setup, we assume that a representative, sparsely annotated training set exists. Trained on this data set, the network densely segments new volumetric images. The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts. The implementation performs on-the-fly elastic deformations for efficient data augmentation during training. It is trained end-to-end from scratch, i.e., no pre-trained network is required. We test the performance of the proposed method on a complex, highly variable 3D structure, the Xenopus kidney, and achieve good results for both use cases.


Convolutional neural networks 3D Biomedical volumetric image segmentation Xenopus kidney Semi-automated Fully-automated Sparse annotation 



We thank the DFG (EXC 294 and CRC-1140 KIDGEM Project Z02 and B07) for supporting this work. Ahmed Abdulkadir acknowledges funding by the grant KF3223201LW3 of the ZIM (Zentrales Innovationsprogramm Mittelstand). Soeren S. Lienkamp acknowledges funding from DFG (Emmy Noether-Programm). We also thank Elitsa Goykovka for the useful annotations and Alena Sammarco for the excellent technical assistance in imaging.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Özgün Çiçek
    • 1
    • 2
    Email author
  • Ahmed Abdulkadir
    • 1
    • 4
  • Soeren S. Lienkamp
    • 2
    • 3
  • Thomas Brox
    • 1
    • 2
  • Olaf Ronneberger
    • 1
    • 2
    • 5
  1. 1.Computer Science DepartmentUniversity of FreiburgFreiburgGermany
  2. 2.BIOSS Centre for Biological Signalling StudiesFreiburgGermany
  3. 3.University Hospital Freiburg, Renal Division, Faculty of MedicineUniversity of FreiburgFreiburgGermany
  4. 4.Department of Psychiatry and PsychotherapyUniversity Medical Center FreiburgFreiburgGermany
  5. 5.Google DeepMindLondonUK

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