ArticlePatterns (New York, N.Y.)2024
A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Million-Scale, Difficulty-Stratified Optical-Microscopy Image Annotation for Neuron Reconstruction.Scientific data · 2026Article
- Image enhancement network based on fiber topology-preserving multi-module fusion for neuron reconstruction.Biomedical optics express · 2025Article
- A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons.Frontiers in neuroinformatics · 2025Article
- A discussion on the application of fluorescence micro-optical sectioning tomography in the research of cognitive dysfunction in diabetes.Open medicine (Warsaw, Poland) · 2025Article
- A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction.Patterns (New York, N.Y.) · 2024Article
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Authors and funding
11 authors.
Funding
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Abstract
Reconstructing neuronal morphology is vital for classifying neurons and mapping brain connectivity. However, it remains a significant challenge due to its complex structure, dense distribution, and low image contrast. In particular, AI-assisted methods often yield numerous errors that require extensive manual intervention. Therefore, reconstructing hundreds of neurons is already a daunting task for general research projects. A key issue is the lack of specialized training for challenging regions due to inadequate data and training methods. This study extracted 2,800 challenging neuronal blocks and categorized them into multiple density levels. Furthermore, we enhanced images using an axial continuity-based network that improved three-dimensional voxel resolution while reducing the difficulty of neuron recognition. Comparing the pre- and post-enhancement results in automatic algorithms using fluorescence micro-optical sectioning tomography (fMOST) data, we observed a significant increase in the recall rate. Our study not only enhances the throughput of reconstruction but also provides a fundamental dataset for tangled neuron reconstruction.
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Registered trials
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