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.
Highlights
Three-dimensional biomedical data sets often contain complex anatomical structures that are difficult to segment and reconstruct
We first show how NetMets can be used evaluate the performance of an automated segmentation algorithm on a data set distributed as part of the DIADEM Challenge
We demonstrate this by creating an incomplete ground truth for a mouse brain microvascular data set imaged using a high-throughput imaging technique called Knife-Edge Scanning Microscopy (KESM) [47]
Summary
Three-dimensional biomedical data sets often contain complex anatomical structures that are difficult to segment and reconstruct. Of particular interest are filament networks embedded in volumetric data. Examples of these include vascular and neuronal networks. With increased use of high-throughput imaging, there has been significant interest in fast and accurate segmentation algorithms for large data sets. Segmentation of filament networks in microscopy data sets continues to be a difficult problem. While there has been an effort to distribute tracking algorithms both commercially and as open source through software packages such as the Farsight Toolkit http://www.farsight-toolkit.org, most algorithms are optimized for specific data sets and imaging modalities. Funding initiatives like the DIADEM Challenge [1] have been designed to motivate
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