ArticleNeuroinformatics2015
Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models.
Article in Neuroinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- 3D+t human sperm flagellum centerline dataset.Scientific data · 2026Article
- Modeling 3D mesoscaled neuronal complexity through learning-based dynamic morphometric convolution.Brain informatics · 2026Article
- DNeuroMAT: A Deep-Learning-Based Neuron Morphology Analysis Toolbox.Methods in molecular biology (Clifton, N.J.) · 2024Article
- BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.Nature methods · 2023Article
- Lung nodule pre-diagnosis and insertion path planning for chest CT images.BMC medical imaging · 2023Article
- SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline.Frontiers in neuroinformatics · 2023Article
- Neuron tracing from light microscopy images: automation, deep learning and bench testing.Bioinformatics (Oxford, England) · 2022Review
- Article
- Retrieving similar substructures on 3D neuron reconstructions.Brain informatics · 2020Article
- Multiphoton intravital microscopy in small animals: motion artefact challenges and technical solutions.Journal of microscopy · 2020Review
- Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites.Neuroinformatics · 2019Article
- Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation.Neuroinformatics · 2019Article
- Cocaine-Induced Preference Conditioning: a Machine Vision Perspective.Neuroinformatics · 2019Article
- FMST: an Automatic Neuron Tracing Method Based on Fast Marching and Minimum Spanning Tree.Neuroinformatics · 2019Article
- Gotta Trace 'em All: A Mini-Review on Tools and Procedures for Segmenting Single Neurons Toward Deciphering the Structural Connectome.Frontiers in bioengineering and biotechnology · 2019Review
- Visualization of Neuronal Structures in Wide-Field Microscopy Brain Images.IEEE transactions on visualization and computer graphics · 2018Article
- Automated sorting of neuronal trees in fluorescent images of neuronal networks using NeuroTreeTracer.Scientific reports · 2018Article
- Automated 3D Soma Segmentation with Morphological Surface Evolution for Neuron Reconstruction.Neuroinformatics · 2018Article
- Automated 3-D Detection of Dendritic Spines from In Vivo Two-Photon Image Stacks.Neuroinformatics · 2017Article
- SparseTracer: the Reconstruction of Discontinuous Neuronal Morphology in Noisy Images.Neuroinformatics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The challenges faced in analyzing optical imaging data from neurons include a low signal-to-noise ratio of the acquired images and the multiscale nature of the tubular structures that range in size from hundreds of microns to hundreds of nanometers. In this paper, we address these challenges and present a computational framework for an automatic, three-dimensional (3D) morphological reconstruction of live nerve cells. The key aspects of this approach are: (i) detection of neuronal dendrites through learning 3D tubular models, and (ii) skeletonization by a new algorithm using a morphology-guided deformable model for extracting the dendritic centerline. To represent the neuron morphology, we introduce a novel representation, the Minimum Shape-Cost (MSC) Tree that approximates the dendrite centerline with sub-voxel accuracy and demonstrate the uniqueness of such a shape representation as well as its computational efficiency. We present extensive quantitative and qualitative results that demonstrate the accuracy and robustness of our method.
Indexed as
Identifiers
25631538What OpenQuestion holds
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.