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Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images

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  • Juan Nunez-Iglesias
  • Ryan Kennedy
  • Toufiq Parag
  • Jianbo Shi
  • Dmitri B Chklovskii

Abstract

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0071715
    DOI: 10.1371/journal.pone.0071715
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    References listed on IDEAS

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    1. 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.
    2. Stephen M Plaza & Louis K Scheffer & Mathew Saunders, 2012. "Minimizing Manual Image Segmentation Turn-Around Time for Neuronal Reconstruction by Embracing Uncertainty," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-14, September.
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