title: Neuron Segmentation in Electron Microscopy Images Using Partial Differential Equations.
abstract: In connectomics, neuroscientists seek to identify the synaptic connections between neurons. Segmentation of cell membranes using supervised learning algorithms on electron microscopy images of brain tissue is often done to assist in this effort. Here we present a partial differential equation with a novel growth term to improve the results of a supervised learning algorithm. We also introduce a new method for representing the resulting image that allows for a more dynamic thresholding to further improve the result. Using these two processes we are able to close small to medium sized gaps in the cell membrane detection and improve the Rand error by as much as 9% over the initial supervised segmentation.
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: Mojtaba
authors.initials: M
authors.lastname: Sayedhosseini
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: biology; connectomics; electron microscopy; partial differential equation