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A quantitative and precision‑oriented neuronal reconstruction approach based on data grading.

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LICENSE at commit 0eac63f, under Apache-2.0 · at the source

Overview

Authors: Mingwei Liao1, Chi Xiao2, Xiaojun Wang2, Qingming Luo2, Hui Gong1,3, Anan Li1,3,2
  1. MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074 China
  2. Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya, 572025 China
  3. HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, 215123 China
Institutions: Wuhan National Laboratory for Optoelectronics (China); Huazhong University of Science and Technology (China); Hainan University (China); Jiangsu Industry Technology Research Institute (China)
Journal: Brain informatics, volume 13, issue 1, article 27
Dates: received 1 March 2026; accepted 10 June 2026; published online 23 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s40708-026-00314-0 · PMID 42334700 · PMCID PMC13328711 · OpenAlex W7165663224
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Smoothing, state filtering, decompositions, Machine learning
Keywords: Neuron reconstruction, Match reconstruction, Allocate reconstruction, The model of neuron reconstruct, Precision reconstruction
Topic: Cell Image Analysis Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: The Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project (2021ZD0201004); the National Natural Science Foundation of China Grants (32192412)
Citations: not cited yet (Europe PMC); 35 references in the paper

Abstract

Accurate and efficient neuronal reconstruction is essential for large-scale neuronal projection analysis and neural circuit mapping. However, conventional reconstruction approaches are often constrained by the structural complexity of neurons, the diversity of imaging signals, and variations in annotator expertise, making it difficult to simultaneously achieve high reconstruction quality and efficiency. To address these challenges, this study proposes a quantitative and precision-oriented neuronal reconstruction framework that systematically integrates reconstruction efficiency and accuracy modeling, data–algorithm matching, and refined task allocation strategies. First, mathematical models were established to quantitatively characterize reconstruction efficiency and accuracy, providing a theoretical foundation for precision reconstruction. Based on quantitative indicators of neuronal reconstruction difficulty, a data–algorithm precise matching strategy was developed to adaptively select the most suitable reconstruction method for different types of neuronal data while leveraging the complementary strengths of multiple reconstruction algorithms. Experimental results demonstrated significant improvements in reconstruction accuracy across multiple data categories, with the best-performing image category achieving an accuracy improvement of up to 18.8%. Furthermore, a data–annotator precise allocation strategy was proposed to match data difficulty with annotator capability, enabling efficient human–machine collaborative reconstruction and transforming conventional experience-based reconstruction into a precision-driven quantitative reconstruction paradigm. Compared with traditional reconstruction strategies, the proposed allocation strategy improved reconstruction accuracy by 44.3% and increased overall reconstruction efficiency by 34.6%. In summary, the proposed framework enables quantitative evaluation and controllable assurance of neuronal reconstruction quality. By transforming neuronal reconstruction from a conventional single-method paradigm into a data-driven precision decision-making paradigm, the proposed approach substantially improves reconstruction efficiency while maintaining high reconstruction quality. This work provides reliable methodological support and a solid data foundation for large-scale neuronal morphology analysis and neural circuit research.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

Brainsmatics/pMatch_code

License: Apache-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0eac63f2ab4c1628b7a1c6283d203fc7ac739973, 1 March 2026
Size: 2 files
Software Heritage: not archived
Found in: “Data availability”
Holds: license file
Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
1 file
At the source: github.com/Brainsmatics/pMatch_code

The paper's code and data availability statement is in the Data section.

Tracing map

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Data

No dataset and no data link were found in the paper.

Data availability

The code for neuron precision-matching reconstruction and neuron precision-allocation reconstruction is available at the following link: https://github.com/Brainsmatics/pMatch_code.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 2 funders, 30 references.

Cite

This paper

Liao, M., Xiao, C., Wang, X., Luo, Q., Gong, H., & Li, A. (2026). A quantitative and precision‑oriented neuronal reconstruction approach based on data grading. Brain informatics, 13(1), 27. https://doi.org/10.1186/s40708-026-00314-0

BibTeX

@article{liao2026quantitative,
author = {Liao, Mingwei and Xiao, Chi and Wang, Xiaojun and Luo, Qingming and Gong, Hui and Li, Anan},
title = {{A quantitative and precision‑oriented neuronal reconstruction approach based on data grading}},
journal = {Brain informatics},
year = {2026},
month = jun,
volume = {13},
number = {1},
pages = {27},
publisher = {Springer},
issn = {2198-4018},
doi = {10.1186/s40708-026-00314-0},
url = {https://doi.org/10.1186/s40708-026-00314-0},
pmid = {42334700},
pmcid = {PMC13328711}
}

RIS

TY - JOUR
AU - Liao, Mingwei
AU - Xiao, Chi
AU - Wang, Xiaojun
AU - Luo, Qingming
AU - Gong, Hui
AU - Li, Anan
TI - A quantitative and precision‑oriented neuronal reconstruction approach based on data grading
T2 - Brain informatics
J2 - Brain Inform
PY - 2026
DA - 2026/06/23
VL - 13
IS - 1
SP - 27
SN - 2198-4018
PB - Springer
DO - 10.1186/s40708-026-00314-0
UR - https://doi.org/10.1186/s40708-026-00314-0
LA - en
ER -

CSL-JSON

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