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This article is part of the supplement: Highlights of the 1st IEEE Symposium on Biological Data Visualization (BioVis 2011)

Open Access Research

NetMets: software for quantifying and visualizing errors in biological network segmentation

David Mayerich1*, Chris Bjornsson2, Jonathan Taylor2 and Badrinath Roysam3

Author Affiliations

1 Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, USA

2 Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, New York, USA

3 Department of Electrical and Computer Engineering, University of Houston, Texas, USA

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BMC Bioinformatics 2012, 13(Suppl 8):S7  doi:10.1186/1471-2105-13-S8-S7

Published: 18 May 2012


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.