A context-aware delayed agglomeration framework for electron microscopy segmentation

PLoS One. 2015 May 27;10(5):e0125825. doi: 10.1371/journal.pone.0125825. eCollection 2015.

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.

MeSH terms

  • Algorithms*
  • Microscopy, Electron*

Grants and funding

The authors have no support or funding to report.