| Issue |
BIO Web Conf.
Volume 237, 2026
2026 8th International Conference on Biotechnology and Biomedicine (ICBB 2026)
|
|
|---|---|---|
| Article Number | 03020 | |
| Number of page(s) | 6 | |
| Section | Biomaterials, Medical Devices and Biomedical Engineering | |
| DOI | https://doi.org/10.1051/bioconf/202623703020 | |
| Published online | 10 June 2026 | |
CARD: Databasing Real-Time Collaborative Reconstructions of 3-D Neuronal Morphologies
School of Biological Science & Medical Engineering, Southeast University, Nanjing, China
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Reconstructing complex three-dimensional (3-D) biological objects (e.g., neurons) from bioimaging data is a key technique in bioimage informatics, with a wide range of applications in biology. Traditional data management systems for neuron reconstruction typically support only static data exports after proofreading, while recent collaborative platforms that enable real-time analysis and proofreading focus primarily on electron microscopy data. Neither adequately addresses the need for dynamic, large-scale collaboration on light-microscopy neuronal morphology. Here we present the Collaborative Augmented Reconstruction Database (CARD), a distributed infrastructure designed for real-time, multi-user collaborative proofreading and annotation of light-microscopy neuronal data across heterogeneous devices. A core technical innovation of CARD is ConMemMap, a contiguous-memory hash map that replaces conventional node-based hash tables with a compact, cache-friendly layout, substantially improving insertion, querying, and deletion throughput while reducing memory consumption. Built upon ConMemMap, CARD integrates real-time quality control for detecting reconstruction errors such as loops and missing branches, a version control module combining user-created snapshots with incremental operation records, and comprehensive metadata management spanning species, brain regions, annotators, devices, and timestamps. Benchmark evaluations demonstrate that the system maintains low latency under high concurrency, supporting efficient collaboration among dozens of simultaneous users on reconstructions containing millions of nodes.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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