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Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images

Author

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  • Anna Kreshuk
  • Christoph N Straehle
  • Christoph Sommer
  • Ullrich Koethe
  • Marco Cantoni
  • Graham Knott
  • Fred A Hamprecht

Abstract

We describe a protocol for fully automated detection and segmentation of asymmetric, presumed excitatory, synapses in serial electron microscopy images of the adult mammalian cerebral cortex, taken with the focused ion beam, scanning electron microscope (FIB/SEM). The procedure is based on interactive machine learning and only requires a few labeled synapses for training. The statistical learning is performed on geometrical features of 3D neighborhoods of each voxel and can fully exploit the high z-resolution of the data. On a quantitative validation dataset of 111 synapses in 409 images of 1948×1342 pixels with manual annotations by three independent experts the error rate of the algorithm was found to be comparable to that of the experts (0.92 recall at 0.89 precision). Our software offers a convenient interface for labeling the training data and the possibility to visualize and proofread the results in 3D. The source code, the test dataset and the ground truth annotation are freely available on the website http://www.ilastik.org/synapse-detection.

Suggested Citation

  • Anna Kreshuk & Christoph N Straehle & Christoph Sommer & Ullrich Koethe & Marco Cantoni & Graham Knott & Fred A Hamprecht, 2011. "Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-8, October.
    • Handle: RePEc:plo:pone00:0024899
    DOI: 10.1371/journal.pone.0024899
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    Cited by:

    1. Victor Kulikov & Syuan-Ming Guo & Matthew Stone & Allen Goodman & Anne Carpenter & Mark Bathe & Victor Lempitsky, 2019. "DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-20, May.
    2. Juan Nunez-Iglesias & Ryan Kennedy & Toufiq Parag & Jianbo Shi & Dmitri B Chklovskii, 2013. "Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-11, August.

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