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A Context-Aware Delayed Agglomeration Framework for Electron Microscopy Segmentation

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  • Toufiq Parag
  • Anirban Chakraborty
  • Stephen Plaza
  • Louis Scheffer

Abstract

Electron Microscopy (EM) image (or volume) segmentation has become significantly important in recent years as an instrument for connectomics. This paper proposes a novel agglomerative framework for EM segmentation. In particular, given an over-segmented image or volume, we propose a novel framework for accurately clustering regions of the same neuron. Unlike existing agglomerative methods, the proposed context-aware algorithm divides superpixels (over-segmented regions) of different biological entities into different subsets and agglomerates them separately. In addition, this paper describes a “delayed” scheme for agglomerative clustering that postpones some of the merge decisions, pertaining to newly formed bodies, in order to generate a more confident boundary prediction. We report significant improvements attained by the proposed approach in segmentation accuracy over existing standard methods on 2D and 3D datasets.

Suggested Citation

  • Toufiq Parag & Anirban Chakraborty & Stephen Plaza & Louis Scheffer, 2015. "A Context-Aware Delayed Agglomeration Framework for Electron Microscopy Segmentation," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-19, May.
    • Handle: RePEc:plo:pone00:0125825
    DOI: 10.1371/journal.pone.0125825
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    References listed on IDEAS

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    1. Shin-ya Takemura & Arjun Bharioke & Zhiyuan Lu & Aljoscha Nern & Shiv Vitaladevuni & Patricia K. Rivlin & William T. Katz & Donald J. Olbris & Stephen M. Plaza & Philip Winston & Ting Zhao & Jane Anne, 2013. "A visual motion detection circuit suggested by Drosophila connectomics," Nature, Nature, vol. 500(7461), pages 175-181, August.
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