ArticleBMC bioinformatics2012
NetMets: software for quantifying and visualizing errors in biological network segmentation.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- Automated Craniofacial Artery Segmentation with Vessel Enhancement-Guided Deep Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Segmentation and modeling of large-scale microvascular networks: a survey.Frontiers in bioinformatics · 2025Review
- Neuron tracing from light microscopy images: automation, deep learning and bench testing.Bioinformatics (Oxford, England) · 2022Review
- Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets.BMC bioinformatics · 2021Article
- Whole-brain tissue mapping toolkit using large-scale highly multiplexed immunofluorescence imaging and deep neural networks.Nature communications · 2021Article
- Improving the Usability of Virtual Reality Neuron Tracing with Topological Elements.IEEE transactions on visualization and computer graphics · 2021Article
- PyNeval: A Python Toolbox for Evaluating Neuron Reconstruction Performance.Frontiers in neuroinformatics · 2021Article
- Automatic Graph-Based Modeling of Brain Microvessels Captured With Two-Photon Microscopy.IEEE journal of biomedical and health informatics · 2019Article
- Robust Tracing and Visualization of Heterogeneous Microvascular Networks.IEEE transactions on visualization and computer graphics · 2019Article
- Brain Capillary Networks Across Species: A few Simple Organizational Requirements Are Sufficient to Reproduce Both Structure and Function.Frontiers in physiology · 2019Article
- System-wide organization of actin cytoskeleton determines organelle transport in hypocotyl plant cells.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles.BMC bioinformatics · 2017Article
- Automated Neuron Tracing Methods: An Updated Account.Neuroinformatics · 2016Review
- NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases.Neuron · 2016Article
- BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies.Neuroinformatics · 2015Article
- Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models.Neuroinformatics · 2015Article
- SOAX: a software for quantification of 3D biopolymer networks.Scientific reports · 2015Article
- A novel method for identifying a graph-based representation of 3-D microvascular networks from fluorescence microscopy image stacks.Medical image analysis · 2015Article
- Modeling brain circuitry over a wide range of scales.Frontiers in neuroanatomy · 2015Review
- Active learning of neuron morphology for accurate automated tracing of neurites.Frontiers in neuroanatomy · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.