Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

High-precision automated reconstruction of neurons with flood-filling networks


Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: The segmentation pipeline, including consensus and agglomeration procedures.
Fig. 2: Inspection-based analysis of segmentation accuracy.
Fig. 3: Quantitative analysis of segmentation accuracy.


  1. Macagno, E. R., Levinthal, C. & Sobel, I. Three-dimensional computer reconstruction of neurons and neuronal assemblies. Annu. Rev. Biophys. Bioeng. 8, 323–351 (1979).

    Article  PubMed  CAS  Google Scholar 

  2. Harris, K. M., Jensen, F. E. & Tsao, B. Three-dimensional structure of dendritic spines and synapses in rat hippocampus (CA1) at postnatal day 15 and adult ages: implications for the maturation of synaptic physiology and long-term potentiation. J. Neurosci. 12, 2685–2705 (1992).

    Article  PubMed  CAS  Google Scholar 

  3. Ventura, R. & Harris, K. M. Three-dimensional relationships between hippocampal synapses and astrocytes. J. Neurosci. 19, 6897–6906 (1999).

    Article  PubMed  CAS  Google Scholar 

  4. Fiala, J. C. Reconstruct: a free editor for serial section microscopy. J. Microsc. 218, 52–61 (2005).

  5. Briggman, K. L. & Bock, D. D. Volume electron microscopy for neuronal circuit reconstruction. Curr. Opin. Neurobiol. 22, 154–161 (2012).

    Article  PubMed  CAS  Google Scholar 

  6. Jain, V., Seung, H. S. & Turaga, S. C. Machines that learn to segment images: a crucial technology for connectomics. Curr. Opin. Neurobiol. 20, 653–666 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Kim, J. S. et al. Space-time wiring specificity supports direction selectivity in the retina. Nature 509, 331–336 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Takemura, S.-Y. et al. Synaptic circuits and their variations within different columns in the visual system of Drosophila. Proc. Natl. Acad. Sci. USA 112, 13711–13716 (2015).

    Article  PubMed  CAS  Google Scholar 

  9. Helmstaedter, M., Briggman, K. L. & Denk, W. High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat. Neurosci. 14, 1081–1088 (2011).

    Article  PubMed  CAS  Google Scholar 

  10. Helmstaedter, M. et al. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500, 168–174 (2013).

    Article  PubMed  CAS  Google Scholar 

  11. Cardona, A. TrakEM2: an ImageJ-based program for morphological data mining and 3D modeling. in Proc. ImageJ User and Developer Conference 18–19 (Centre de Recherche Public Henri Tudor: Luxembourg, 2006).

  12. Berning, M., Boergens, K. M. & Helmstaedter, M. SegEM: efficient image analysis for high-resolution connectomics. Neuron 87, 1193–1206 (2015).

    Article  PubMed  CAS  Google Scholar 

  13. Plaza, S. M. Focused proofreading to reconstruct neural connectomes from EM images at scale. in Deep Learning and Data Labeling for Medical Applications (eds. Carneiro, G. et al.) 249–258 (Springer, Cham, 2016).

  14. Jain, V. et al. Supervised learning of image restoration with convolutional networks. in Proc. IEEE 11th International Conference on Computer Vision 636–643 (IEEE, New York, 2007).

  15. Ciresan, D., Giusti, A., Gambardella, L. M. & Schmidhuber, J. Deep neural networks segment neuronal membranes in electron microscopy images. in Advances in Neural Information Processing Systems 25 (eds. Pereira, F. et al.) 2852–2860 (Neural Information Processing Systems Foundation, La Jolla, CA, 2012).

  16. Turaga, S. C. et al. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput. 22, 511–538 (2010).

    Article  PubMed  Google Scholar 

  17. Funke, J. et al. A deep structured learning approach towards automating connectome reconstruction from 3D electron micrographs. arXiv Preprint at (2017).

  18. Lee, K., Zung, J., Li, P., Jain, V. & Seung, H. S. Superhuman accuracy on the SNEMI3D Connectomics Challenge. arXiv Preprint at (2017).

  19. Andres, B., Koethe, U., Helmstaedter, M., Denk, W. & Hamprecht, F. A. Segmentation of SBFSEM volume data of neural tissue by hierarchical classification. in Pattern Recognition: Proceedings of the 30th DAGM Symposium (ed. Rigoll, G.)142–152 (Springer, Berlin, 2008).

  20. Kaynig, V. et al. Large-scale automatic reconstruction of neuronal processes from electron microscopy images. Med. Image Anal. 22, 77–88 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Knowles-Barley, S. et al. RhoanaNet pipeline: dense automatic neural annotation. arXiv Preprint at (2016).

  22. Beier, T. et al. Multicut brings automated neurite segmentation closer to human performance. Nat. Methods 14, 101–102 (2017).

    Article  PubMed  CAS  Google Scholar 

  23. Turaga, S. C., Briggman, K. L., Helmstaedter, M., Denk, W. & Seung, H. S. Maximin affinity learning of image segmentation. in Advances in Neural Information Processing Systems 22 (eds. Bengio, Y. et al.) 1865–1873 (Neural Information Processing Systems, La Jolla, CA, 2009).

  24. Jain, V. et al. Boundary learning by optimization with topological constraints. in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2488–2495 (IEEE, New York, 2010).

  25. Denk, W. & Horstmann, H. Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol. 2, e329 (2004).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, 2016).

    Google Scholar 

  27. Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional networks for biomedical image segmentation. in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015 (eds. Navab, N. et al.) 234–241 (Springer, Cham, 2015).

  28. Nunez-Iglesias, J., Kennedy, R., Plaza, S. M., Chakraborty, A. & Katz, W. T. Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages. Front. Neuroinform. 8, 34 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Maitin-Shepard, J., Jain, V., Januszewski, M., Li, P. & Abbeel, P. Combinatorial energy learning for image segmentation. arXiv Preprint at (2015).

  30. Saalfeld, S., Fetter, R., Cardona, A. & Tomancak, P. Elastic volume reconstruction from series of ultra-thin microscopy sections. Nat. Methods 9, 717–720 (2012).

    Article  PubMed  CAS  Google Scholar 

  31. Dorkenwald, S. et al. Automated synaptic connectivity inference for volume electron microscopy. Nat. Methods 14, 435–442 (2017).

    Article  PubMed  CAS  Google Scholar 

  32. Pallotto, M., Watkins, P. V., Fubara, B., Singer, J. H. & Briggman, K. L. Extracellular space preservation aids the connectomic analysis of neural circuits. eLife 4, e08206 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zlateski, A. & Seung, H. S. Image segmentation by size-dependent single linkage clustering of a watershed basin graph. arXiv Preprint at (2015).

  34. Kasthuri, N. et al. Saturated reconstruction of a volume of neocortex. Cell 162, 648–661 (2015).

    Article  PubMed  CAS  Google Scholar 

  35. Martin, D. R., Fowlkes, C. C. & Malik, J. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26, 530–549 (2004).

    Article  PubMed  Google Scholar 

  36. Funke, J., Andres, B., Hamprecht, F. A., Cardona, A. & Cook, M. Efficient automatic 3D-reconstruction of branching neurons from EM data. in Proc. IEEE Conference on Computer Vision and Pattern Recognition 1004–1011 (IEEE, New York, 2012).

  37. Wolf, S., Schott, L., Kothe, U. & Hamprecht, F. Learned watershed: end-to-end learning of seeded segmentation. in Proc. IEEE International Conference on Computer Vision (ICCV) 2030–2038 (IEEE, New York, 2017).

  38. Bai, M. & Urtasun, R. Deep watershed transform for instance segmentation. in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2858–2866 (IEEE, New York, 2017).

  39. Meirovitch, Y. et al. A multi-pass approach to large-scale connectomics. arXiv Preprint at (2016).

  40. Romera-Paredes, B. & Torr, P. H. S. Recurrent instance segmentation. in Computer Vision—ECCV 2016 (eds. Leibe, B. et al.) 312–329 (Springer, Cham, 2016).

    Chapter  Google Scholar 

  41. Pinheiro, P. O , Lin, T.-Y., Collobert, R., & Dollár, P. Learning to refine object segments. in Computer Vision–ECCV 2016 (eds. Leibe, B. et al.) 75–91 (Springer, Cham, 2016).

  42. Ren, M. & Zemel, R. S. End-to-end instance segmentation with recurrent attention. in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 293–301 (IEEE, New York, 2017).

  43. Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv Preprint at (2016).

  44. LeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989).

    Article  Google Scholar 

  45. LeCun, Y. A , Bottou, L , Orr, G. B., & Müller, K.-R . Efficient BackProp. in Neural Networks: Tricks of the Trade (eds. Montavon, G., Orr, G. & Müller, K.-R.) 9–48 (Springer, Berlin, 2012).

  46. Tschopp, F. Efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems. arXiv Preprint at (2015).

  47. He, K., Zhang, X., Ren, S. & Sun, J. Identity mappings in deep residual networks. arXiv Preprint at (2016).

  48. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. arXiv Preprint at (2015).

  49. Kornfeld, J. et al. EM connectomics reveals axonal target variation in a sequence-generating network. eLife 6, e24364 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

Download references


We thank T.L. Dean and B. Agüera y Arcas for useful discussions and support. We also thank M.S. Fee and J. Shlens for comments on the manuscript.

Author information

Authors and Affiliations



M.J. and V.J. conceived FFNs. M.J. developed pipelines and performed experiments. J.K. and W.D. acquired EM data and ground truth annotations. A.P. aligned the dataset. M.J. and J.K. analyzed results. V.J., M.J., W.D., P.H.L., J.M.-S., and J.K. developed skeleton metrics. M.J. and V.J. developed tissue-classification models. T.B., L.L., M.T., and J.M. developed software for annotation and visualization. V.J., M.J., W.D., and J.K. wrote the manuscript. V.J. supervised the project.

Corresponding author

Correspondence to Viren Jain.

Ethics declarations

Competing interests

J.K. holds shares of Ariadne service GmbH. M.J., P.H.L., A.P., T.B., L.L., J.M.-S., M.T., and V.J. are employees of Google LLC, which sells cloud computing services.

Additional information

Publishers note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Tissue classification results.

Manual annotations (left) and convolutional network inference (right) of a subset of the labeled voxel classes: blood vessel (red), myelin (blue), and cell body (green). False positive identifications of cell body voxels are visible in the automated inference (inside the myelinated area). Scale bar is 2 microns.

Supplementary Figure 2 Architecture of the FFN.

Overall computational architecture of the Flood-filling Network. Each of the eight convolutional modules are identical and implement the operations shown in the top inset box. The predicted object map (POM) is shown as the yellow mask channel, and provides recurrent feedback to the FFN.

Supplementary Information

Supplementary Text and Figures

Supplementary Figs. 1 and 2 and Supplementary Note

Reporting Summary

Supplementary Video 1

FFN reconstruction of a single neurite (i.e., seeded from a single voxel) in J0126 volume

Supplementary Software

Flood-filling network software for training and inference

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Januszewski, M., Kornfeld, J., Li, P.H. et al. High-precision automated reconstruction of neurons with flood-filling networks. Nat Methods 15, 605–610 (2018).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing