ArticleBioinformatics (Oxford, England)2011
Automatic 3D neuron tracing using all-path pruning.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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66 citing papers in PubMed.
- Clustered inputs engage dendritic nonlinearities and calcium signaling to support efficient place-field formation in CA1 pyramidal neurons.Cell reports · 2026Article
- Modeling 3D mesoscaled neuronal complexity through learning-based dynamic morphometric convolution.Brain informatics · 2026Article
- Automatic and accurate reconstruction of long-range axonal projections of single-neuron in mouse brain.eLife · 2025Article
- From Morphology to Computation: How Synaptic Organization Shapes Place Fields in CA1 Pyramidal Neurons.bioRxiv : the preprint server for biology · 2025Article
- A Multi-Scale Neuron Morphometry Dataset from Peta-voxel Mouse Whole-Brain Images.Scientific data · 2025Article
- Collaborative augmented reconstruction of 3D neuron morphology in mouse and human brains.Nature methods · 2024Article
- NIEND: neuronal image enhancement through noise disentanglement.Bioinformatics (Oxford, England) · 2024Article
- Article
- DNeuroMAT: A Deep-Learning-Based Neuron Morphology Analysis Toolbox.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images.bioRxiv : the preprint server for biology · 2023Article
- BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.Nature methods · 2023Article
- NRRS: a re-tracing strategy to refine neuron reconstruction.Bioinformatics advances · 2023Article
- SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline.Frontiers in neuroinformatics · 2023Article
- Tracing weak neuron fibers.Bioinformatics (Oxford, England) · 2023Article
- Neuron tracing from light microscopy images: automation, deep learning and bench testing.Bioinformatics (Oxford, England) · 2022Review
- NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images.IEEE transactions on visualization and computer graphics · 2022Article
- Smart imaging to empower brain-wide neuroscience at single-cell levels.Brain informatics · 2022Review
- Neuron segmentation using 3D wavelet integrated encoder-decoder network.Bioinformatics (Oxford, England) · 2022Article
- Exploring highly reliable substructures in auto-reconstructions of a neuron.Brain informatics · 2021Article
- Improving the Usability of Virtual Reality Neuron Tracing with Topological Elements.IEEE transactions on visualization and computer graphics · 2021Article
6 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
Abstract
motivationDigital reconstruction, or tracing, of 3D neuron structures is critical toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low signal-to-noise ratio (SNR) and fragmented neuron segments. Published work can handle these hard situations only by introducing global prior information, such as where a neurite segment starts and terminates. However, manual incorporation of such global information can be very time consuming. Thus, a completely automatic approach for these hard situations is highly desirable.
resultsWe have developed an automatic graph algorithm, called the all-path pruning (APP), to trace the 3D structure of a neuron. To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image. Since the initial reconstruction contains all the possible paths and thus could contain redundant structural components (SC), we simplify the entire reconstruction without compromising its connectedness by pruning the redundant structural elements, using a new maximal-covering minimal-redundant (MCMR) subgraph algorithm. We show that MCMR has a linear computational complexity and will converge. We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly). AVAILABILITY: The software is available upon request. We plan to eventually release the software as a plugin of the V3D-Neuron package at http://penglab.janelia.org/proj/v3d. CONTACT: pengh@janelia.hhmi.org.
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