ReviewNeuroinformatics2016
Automated Neuron Tracing Methods: An Updated Account.
Review in Neuroinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 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
45 citing papers in PubMed.
- MASCAF: A Cable Model Fitting Pipeline for Topologically Complex Surface Meshes.bioRxiv : the preprint server for biology · 2026Article
- From Pixels to Patterns: A Multidimensional Framework to Decode Cytoskeletal Organization.Computational and structural biotechnology journal · 2026Review
- Frenet-Serret Frame-Based Decomposition for Part Segmentation of 3-D Curvilinear Structures.IEEE transactions on medical imaging · 2025Article
- Image enhancement network based on fiber topology-preserving multi-module fusion for neuron reconstruction.Biomedical optics express · 2025Article
- A Multi-Scale Neuron Morphometry Dataset from Peta-voxel Mouse Whole-Brain Images.Scientific data · 2025Article
- Fibre tracing in biomedical images: An objective comparison between seven algorithms.PloS one · 2025Article
- Collaborative augmented reconstruction of 3D neuron morphology in mouse and human brains.Nature methods · 2024Article
- From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation.Communications biology · 2024Review
- Neuron tracing from light microscopy images: automation, deep learning and bench testing.Bioinformatics (Oxford, England) · 2022Review
- Smart imaging to empower brain-wide neuroscience at single-cell levels.Brain informatics · 2022Review
- Hidden Markov modeling for maximum probability neuron reconstruction.Communications biology · 2022Article
- Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains.Neuroinformatics · 2022Article
- Quantitative mapping of keratin networks in 3D.eLife · 2022Article
- Comparing Automated Morphology Quantification Software on Dendrites of Uninjured and Injured Drosophila Neurons.Neuroinformatics · 2021Article
- Expansion microscopy: A powerful nanoscale imaging tool for neuroscientists.Neurobiology of disease · 2021Review
- An imaging analysis protocol to trace, quantify, and model multi-signal neuron morphology.STAR protocols · 2021Article
- Improving the Usability of Virtual Reality Neuron Tracing with Topological Elements.IEEE transactions on visualization and computer graphics · 2021Article
- Correction of topological errors in automated traces of neurites.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- PyNeval: A Python Toolbox for Evaluating Neuron Reconstruction Performance.Frontiers in neuroinformatics · 2021Article
- Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review.Computational and structural biotechnology journal · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The reconstruction of neuron morphology allows to investigate how the brain works, which is one of the foremost challenges in neuroscience. This process aims at extracting the neuronal structures from microscopic imaging data. The great advances in microscopic technologies have made a huge amount of data available at the micro-, or even lower, resolution where manual inspection is time consuming, prone to error and utterly impractical. This has motivated the development of methods to automatically trace the neuronal structures, a task also known as neuron tracing. This paper surveys the latest neuron tracing methods available in the scientific literature as well as a selection of significant older papers to better place these proposals into context. They are categorized into global processing, local processing and meta-algorithm approaches. Furthermore, we point out the algorithmic components used to design each method and we report information on the datasets and the performance metrics used.
Indexed as
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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.