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Arganda-Carreras I, Turaga SC, Berger DR, et al. Crowdsourcing the creation of image segmentation algorithms for connectomics. Front Neuroanat. 2015;9:142doi: 10.3389/fnana.2015.00142.
Arganda-Carreras, I., Turaga, S. C., Berger, D. R., Cireşan, D., Giusti, A., Gambardella, L. M., Schmidhuber, J., Laptev, D., Dwivedi, S., Buhmann, J. M., Liu, T., Seyedhosseini, M., Tasdizen, T., Kamentsky, L., Burget, R., Uher, V., Tan, X., Sun, C., Pham, T. D., Bas, E., Uzunbas, M. G., Cardona, A., Schindelin, J., & Seung, H. S. (2015). Crowdsourcing the creation of image segmentation algorithms for connectomics. Frontiers in neuroanatomy, 9142. https://doi.org/10.3389/fnana.2015.00142
Arganda-Carreras, Ignacio, et al. "Crowdsourcing the creation of image segmentation algorithms for connectomics." Frontiers in neuroanatomy vol. 9 (2015): 142. doi: https://doi.org/10.3389/fnana.2015.00142
Arganda-Carreras I, Turaga SC, Berger DR, Cireşan D, Giusti A, Gambardella LM, Schmidhuber J, Laptev D, Dwivedi S, Buhmann JM, Liu T, Seyedhosseini M, Tasdizen T, Kamentsky L, Burget R, Uher V, Tan X, Sun C, Pham TD, Bas E, Uzunbas MG, Cardona A, Schindelin J, Seung HS. Crowdsourcing the creation of image segmentation algorithms for connectomics. Front Neuroanat. 2015 Nov 05;9:142. doi: 10.3389/fnana.2015.00142. eCollection 2015. PMID: 26594156; PMCID: PMC4633678.
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Front Neuroanat. 2015 Nov 05;9:142. doi: 10.3389/fnana.2015.00142. eCollection 2015.

Crowdsourcing the creation of image segmentation algorithms for connectomics.

Frontiers in neuroanatomy

Ignacio Arganda-Carreras, Srinivas C Turaga, Daniel R Berger, Dan Cireşan, Alessandro Giusti, Luca M Gambardella, Jürgen Schmidhuber, Dmitry Laptev, Sarvesh Dwivedi, Joachim M Buhmann, Ting Liu, Mojtaba Seyedhosseini, Tolga Tasdizen, Lee Kamentsky, Radim Burget, Vaclav Uher, Xiao Tan, Changming Sun, Tuan D Pham, Erhan Bas, Mustafa G Uzunbas, Albert Cardona, Johannes Schindelin, H Sebastian Seung

Affiliations

  1. UMR1318 French National Institute for Agricultural Research-AgroParisTech, French National Institute for Agricultural Research Centre de Versailles-Grignon, Institut Jean-Pierre Bourgin Versailles, France.
  2. Howard Hughes Medical Institute, Janelia Research Campus Ashburn, VA, USA.
  3. Center for Brain Science, Harvard University Cambridge, MA, USA.
  4. Swiss AI Lab IDSIA (Dalle Molle Institute for Artificial Intelligence) Universitá Della Svizzera Italiana, Scuola Universitaria Professionale Della Svizzera Italiana Lugano, Switzerland.
  5. Department of Computer Science, ETH Zurich Zurich, Switzerland.
  6. Scientific Computing and Imaging Institute, University of Utah Salt Lake City, UT, USA.
  7. Imaging Platform, Broad Institute Cambridge, MA, USA.
  8. Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology Brno, Czech Republic.
  9. School of Engineering and Information Technology, University of New South Wales Canberra, ACT, Australia.
  10. Digital Productivity Flagship, Commonwealth Scientific and Industrial Research Organisation North Ryde, NSW, Australia.
  11. Department of Biomedical Engineering, The Institute of Technology, Linkoping University Linkoping, Sweden.
  12. Computer Science Department, Rutgers University New Brunswick, NJ, USA.
  13. Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison Madison, WI, USA.
  14. Princeton Neuroscience Institute and Computer Science Department, Princeton University Princeton, NJ, USA.

PMID: 26594156 PMCID: PMC4633678 DOI: 10.3389/fnana.2015.00142

Abstract

To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.

Keywords: connectomics; electron microscopy; image segmentation; machine learning; reconstruction

References

  1. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):419-27 - PubMed
  2. Curr Opin Neurobiol. 2010 Oct;20(5):653-66 - PubMed
  3. IEEE Trans Pattern Anal Mach Intell. 2011 May;33(5):898-916 - PubMed
  4. Nature. 2014 May 15;509(7500):331-6 - PubMed
  5. Nat Methods. 2014 Mar;11(3):281-9 - PubMed
  6. IEEE Trans Pattern Anal Mach Intell. 2007 Jun;29(6):929-44 - PubMed
  7. Neuron. 2010 Sep 23;67(6):1009-20 - PubMed
  8. Nat Biotechnol. 2013 Feb;31(2):108-11 - PubMed
  9. Curr Opin Neurobiol. 2010 Oct;20(5):667-75 - PubMed
  10. Nature. 2013 Aug 8;500(7461):168-74 - PubMed
  11. Neuron. 2012 Jun 7;74(5):816-29 - PubMed
  12. Philos Trans R Soc Lond B Biol Sci. 1986 Nov 12;314(1165):1-340 - PubMed
  13. PLoS One. 2013 Aug 20;8(8):e71715 - PubMed
  14. Nature. 2011 Mar 10;471(7337):183-8 - PubMed
  15. PLoS Biol. 2010 Oct 05;8(10):null - PubMed
  16. J Struct Biol. 2005 Jul;151(1):41-60 - PubMed
  17. Med Image Anal. 2015 May;22(1):77-88 - PubMed
  18. Nat Methods. 2012 Jun 28;9(7):676-82 - PubMed
  19. Science. 2012 Jul 27;337(6093):437-44 - PubMed
  20. Nature. 2013 Aug 8;500(7461):175-81 - PubMed
  21. Curr Opin Neurobiol. 2012 Jun;22(3):372-82 - PubMed
  22. IEEE Trans Pattern Anal Mach Intell. 2004 May;26(5):530-49 - PubMed
  23. Cell. 2013 Jan 17;152(1-2):109-19 - PubMed
  24. Cell. 2015 Jul 30;162(3):648-61 - PubMed
  25. PLoS One. 2012;7(6):e38011 - PubMed

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