ArticleNature methods2023
BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
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22 citing papers in PubMed.
- A Million-Scale, Difficulty-Stratified Optical-Microscopy Image Annotation for Neuron Reconstruction.Scientific data · 2026Article
- SegAnyNeuron: a neural image segmentation network with strong generalization performance by modeling image intensity variation.Brain informatics · 2026Article
- Automated Proofreading of Digitally Reconstructed Neural Morphology Enhances Accuracy, Scalability, and Standardization.bioRxiv : the preprint server for biology · 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
- A Multi-Scale Neuron Morphometry Dataset from Peta-voxel Mouse Whole-Brain Images.Scientific data · 2025Article
- Connectivity of single neurons classifies cell subtypes in mouse brains.Nature methods · 2025Article
- A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons.Frontiers in neuroinformatics · 2025Article
- Neuronal diversity and stereotypy at multiple scales through whole brain morphometry.Nature communications · 2024Article
- WASPSYN: A Challenge for Domain Adaptive Synapse Detection in Microwasp Brain Connectomes.IEEE transactions on medical imaging · 2024Article
- Collaborative augmented reconstruction of 3D neuron morphology in mouse and human brains.Nature methods · 2024Article
- A FAIR, open-source virtual reality platform for dendritic spine analysis.Patterns (New York, N.Y.) · 2024Article
- High-throughput analysis of dendrite and axonal arbors reveals transcriptomic correlates of neuroanatomy.Nature communications · 2024Article
- Sharing massive biomedical data at magnitudes lower bandwidth using implicit neural function.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Accelerating the continuous community sharing of digital neuromorphology data.FASEB bioAdvances · 2024Article
- Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.Brain informatics · 2024Review
- A modular framework for multi-scale tissue imaging and neuronal segmentation.Nature communications · 2024Article
- From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation.Communications biology · 2024Review
- Bibliometric analysis of neuroscience publications quantifies the impact of data sharing.Bioinformatics (Oxford, England) · 2023Article
- Online conversion of reconstructed neural morphologies into standardized SWC format.Nature communications · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
65 authors.
Funding
Abstract
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.
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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.