ReviewNeuroinformatics2011
The DIADEM data sets: representative light microscopy images of neuronal morphology to advance automation of digital reconstructions.
Review in Neuroinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 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
60 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
- Modeling 3D mesoscaled neuronal complexity through learning-based dynamic morphometric convolution.Brain informatics · 2026Article
- A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons.Frontiers in neuroinformatics · 2025Article
- A FAIR, open-source virtual reality platform for dendritic spine analysis.Patterns (New York, N.Y.) · 2024Article
- A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction.Patterns (New York, N.Y.) · 2024Article
- High-throughput analysis of dendrite and axonal arbors reveals transcriptomic correlates of neuroanatomy.Nature communications · 2024Article
- Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.Brain informatics · 2024Review
- From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation.Communications biology · 2024Review
- DNeuroMAT: A Deep-Learning-Based Neuron Morphology Analysis Toolbox.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Tracing weak neuron fibers.Bioinformatics (Oxford, England) · 2023Article
- Neuron tracing from light microscopy images: automation, deep learning and bench testing.Bioinformatics (Oxford, England) · 2022Review
- Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda.Neuroinformatics · 2021Article
- GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population.Neuroinformatics · 2021Article
- Correction of topological errors in automated traces of neurites.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Improving the Usability of Virtual Reality Neuron Tracing with Topological Elements.IEEE transactions on visualization and computer graphics · 2021Article
- CREB3L2 Modulates Nerve Growth Factor-Induced Cell Differentiation.Frontiers in molecular neuroscience · 2021Article
- Foreground Estimation in Neuronal Images With a Sparse-Smooth Model for Robust Quantification.Frontiers in neuroanatomy · 2021Article
- BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows.Patterns (New York, N.Y.) · 2020Article
- Brain-Wide Shape Reconstruction of a Traced Neuron Using the Convex Image Segmentation Method.Neuroinformatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
The comprehensive characterization of neuronal morphology requires tracing extensive axonal and dendritic arbors imaged with light microscopy into digital reconstructions. Considerable effort is ongoing to automate this greatly labor-intensive and currently rate-determining process. Experimental data in the form of manually traced digital reconstructions and corresponding image stacks play a vital role in developing increasingly more powerful reconstruction algorithms. The DIADEM challenge (short for DIgital reconstruction of Axonal and DEndritic Morphology) successfully stimulated progress in this area by utilizing six data set collections from different animal species, brain regions, neuron types, and visualization methods. The original research projects that provided these data are representative of the diverse scientific questions addressed in this field. At the same time, these data provide a benchmark for the types of demands automated software must meet to achieve the quality of manual reconstructions while minimizing human involvement. The DIADEM data underwent extensive curation, including quality control, metadata annotation, and format standardization, to focus the challenge on the most substantial technical obstacles. This data set package is now freely released ( http://diademchallenge.org ) to train, test, and aid development of automated reconstruction algorithms.
Indexed as
Identifiers
What 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.