title: Semi-Automatic Neuron Segmentation in Electron Microscopy Images Via Sparse Labeling.
abstract: We introduce a novel method for utilizing user input to sparsely label membranes in electron microscopy images. Using gridlines as guides, the user marks where the guides cross the membrane to generate a sparsely labeled image. We use a best path algorithm to connect each of the sparse membrane labels. The resulting segmentation has a significantly better Rand error than automatic methods while requiring as little as 2% of the image to be labeled.
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah.
authors.forename: Cory
authors.initials: C
authors.lastname: Jones
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah.
authors.forename: Ting
authors.initials: T
authors.lastname: Liu
authors.affiliation: National Center for Microscopy and Imaging Research, Univeristy of California, San Diego.
authors.forename: Mark
authors.initials: M
authors.lastname: Ellisman
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah.
authors.forename: Tolga
authors.initials: T
authors.lastname: Tasdizen
keywords: biological segmentation; connectomics; electron microscopy; semi-automatic segmentation