Paper Info
Title
NetMets: software for quantifying and visualizing errors in biological network segmentation.
Abstract
One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization.
Year
DOI
Venue
2012
10.1186/1471-2105-13-S8-S7
BMC Bioinformatics
Keywords
Field
DocType
algorithms,bioinformatics,microarrays
Data set,Biological network,Segmentation,Computer science,Visualization,Image processing,Software,Hausdorff distance,Bioinformatics,Network model
Journal
Volume
Issue
ISSN
13 Suppl 8
S-8
1471-2105
Citations 
PageRank 
References 
14
0.53
30
Authors
4
Name
Order
Citations
PageRank
David Mayerich17510.05
Chris Bjornsson2140.53
Jonathan Taylor31034.93
Badrinath Roysam41890113.97