ArticleNeuroinformatics2013
Semi-automated neuron boundary detection and nonbranching process segmentation in electron microscopy images.
Article in Neuroinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Deep Learning-Driven Transmission Electron Microscopy Analysis of Murine Optic Nerve Myelinated Axons.Ophthalmology science · 2026Article
- Membrane mechanics dictate axonal pearls-on-a-string morphology and function.Nature neuroscience · 2025Article
- VolRoverN: enhancing surface and volumetric reconstruction for realistic dynamical simulation of cellular and subcellular function.Neuroinformatics · 2014Article
- Automated detection of synapses in serial section transmission electron microscopy image stacks.PloS one · 2014Article
- Segmentation of neuronal structures using SARSA (λ)-based boundary amendment with reinforced gradient-descent curve shape fitting.PloS one · 2014Article
- Article
- Biomedical Visual Computing: Case Studies and Challenges.Computing in science & engineering · 2012Article
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7 authors.
Funding
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.
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Registered trials
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