ArticleNeuroinformatics2020
Brain-Wide Shape Reconstruction of a Traced Neuron Using the Convex Image Segmentation Method.
Article in Neuroinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 18 citations in OpenAlex.
- A high-resolution dataset of mouse brain vasculature for deep learning-based reconstruction.Frontiers in neuroinformatics · 2026Article
- BVLab-analyzer: a unified visual toolbox for terabyte-scale whole-brain cell detection, neuron reconstruction, and vascular network extraction.Frontiers in neuroinformatics · 2026Article
- Automatic and accurate reconstruction of long-range axonal projections of single-neuron in mouse brain.eLife · 2025Article
- Gapr for large-scale collaborative single-neuron reconstruction.Nature methods · 2024Article
- Preserving Derivative Information while Transforming Neuronal Curves.Neuroinformatics · 2024Article
- Neuron tracing from light microscopy images: automation, deep learning and bench testing.Bioinformatics (Oxford, England) · 2022Review
- Hidden Markov modeling for maximum probability neuron reconstruction.Communications biology · 2022Article
- GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population.Neuroinformatics · 2021Article
- Dl-3-N-Butylphthalide Promotes Angiogenesis in an Optimized Model of Transient Ischemic Attack in C57BL/6 Mice.Frontiers in pharmacology · 2021Article
- Weakly Supervised Learning of 3D Deep Network for Neuron Reconstruction.Frontiers in neuroanatomy · 2020Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuronal shape reconstruction is a helpful technique for establishing neuron identity, inferring neuronal connections, mapping neuronal circuits, and so on. Advances in optical imaging techniques have enabled data collection that includes the shape of a neuron across the whole brain, considerably extending the scope of neuronal anatomy. However, such datasets often include many fuzzy neurites and many crossover regions that neurites are closely attached, which make neuronal shape reconstruction more challenging. In this study, we proposed a convex image segmentation model for neuronal shape reconstruction that segments a neurite into cross sections along its traced skeleton. Both the sparse nature of gradient images and the rule that fuzzy neurites usually have a small radius are utilized to improve neuronal shape reconstruction in regions with fuzzy neurites. Because the model is closely related to the traced skeleton point, we can use this relationship for identifying neurite with crossover regions. We demonstrated the performance of our model on various datasets, including those with fuzzy neurites and neurites with crossover regions, and we verified that our model could robustly reconstruct the neuron shape on a brain-wide scale.
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