title: A context-aware delayed agglomeration framework for electron microscopy segmentation.
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.
authors.affiliation: Janelia Research Campus, HHMI, Ashburn, VA, USA.
authors.forename: Toufiq
authors.initials: T
authors.lastname: Parag
authors.affiliation: Department of Diagnostic Radiology, National University of Singapore, Singapore.
authors.forename: Anirban
authors.initials: A
authors.lastname: Chakraborty
authors.affiliation: Janelia Research Campus, HHMI, Ashburn, VA, USA.
authors.forename: Stephen
authors.initials: S
authors.lastname: Plaza
authors.affiliation: Janelia Research Campus, HHMI, Ashburn, VA, USA.
authors.forename: Louis
authors.initials: L
authors.lastname: Scheffer
mesh_terms: D000465:Algorithms; D008854:Microscopy, Electron