Semi-automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

Overview

Journal Neuroinformatics

Specialties Medical Informatics
Neurology

Date 2012 May 31

PMID 22644867

Citations 6

Authors

Elizabeth Jurrus

Shigeki Watanabe

Richard J Giuly

Antonio R C Paiva

Mark H Ellisman

Erik M Jorgensen

Tolga Tasdizen

Affiliations

Soon will be listed here.

Abstract

Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes.

Citing Articles

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