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Peer-reviewedCC BYFrontiers in neuroinformatics

BVLab-analyzer: a unified visual toolbox for terabyte-scale whole-brain cell detection, neuron reconstruction, and vascular network extraction.

  • Ying Hu1,2
  • Xiaojun Wang3
  • Pei Sun4
  • Yuan Shen5
  • Tingwei Quan5
5 affiliations
  1. 1School of Mathematics and Statistics, Hubei University of Education, Wuhan, Hubei, China.
  2. 2Institute of Big Data Analysis and Applied Mathematics, Hubei University of Education, Wuhan, Hubei, China.
  3. 3Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya, China.
  4. 4Department of Clinical Research Institute, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China.
  5. 5Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, China.
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Abstract

BVLab-Analyzer is a standalone graphical software tool for end-to-end analysis of terabyte-scale whole-brain microscopy images. Operating through a zero-code interface, it handles data import (TIFF, BioImageVision, OME-Zarr), deep learning-based semantic segmentation, 3D skeleton reconstruction, and morphological statistical analysis within a unified pipeline. The software supports three target structures-cells, neurons and vasculature-and exports their morphological reconstructions in standard SWC format. To accelerate training data preparation, Smart Annotation tools provide sub-volume interference isolation, coarse-vessel ray tracing, and skeleton auto-interpolation. For inference, optimized block-wise strategies enable a standard 3D U-Net to predict whole-brain volumes up to 12 TB on a single consumer-grade GPU, while an interrupt-and-resume engine ensures fault tolerance during extended computations. By integrating these features, BVLab-Analyzer lowers the hardware barrier for whole-brain analysis and unifies previously fragmented toolchains-from annotation and segmentation to reconstruction and quantification-into a single, accessible software environment.

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Cite

BibTeXRIS
BibTeX
@article{hu2026bvlabanalyzer,
  title = {{BVLab-analyzer: a unified visual toolbox for terabyte-scale whole-brain cell detection, neuron reconstruction, and vascular network extraction.}},
  author = {Ying Hu and Xiaojun Wang and Pei Sun and Yuan Shen and Tingwei Quan},
  journal = {Frontiers in neuroinformatics},
  year = {2026},
  month = oct,
  doi = {10.3389/fninf.2026.1925112},
  url = {https://europepmc.org/article/MED/42908880},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - BVLab-analyzer: a unified visual toolbox for terabyte-scale whole-brain cell detection, neuron reconstruction, and vascular network extraction.
AU  - Ying Hu
AU  - Xiaojun Wang
AU  - Pei Sun
AU  - Yuan Shen
AU  - Tingwei Quan
PY  - 2026
DA  - 2026/10/07
JO  - Frontiers in neuroinformatics
DO  - 10.3389/fninf.2026.1925112
UR  - https://europepmc.org/article/MED/42908880
AB  - BVLab-Analyzer is a standalone graphical software tool for end-to-end analysis of terabyte-scale whole-brain microscopy images. Operating through a zero-code interface, it handles data import (TIFF, BioImageVision, OME-Zarr), deep learning-based semantic segmentation, 3D skeleton reconstruction, and morphological statistical analysis within a unified pipeline. The software supports three target structures-cells, neurons and vasculature-and exports their morphological reconstructions in standard SWC format. To accelerate training data preparation, Smart Annotation tools provide sub-volume interference isolation, coarse-vessel ray tracing, and skeleton auto-interpolation. For inference, optimized block-wise strategies enable a standard 3D U-Net to predict whole-brain volumes up to 12 TB on a single consumer-grade GPU, while an interrupt-and-resume engine ensures fault tolerance during extended computations. By integrating these features, BVLab-Analyzer lowers the hardware barrier for whole-brain analysis and unifies previously fragmented toolchains-from annotation and segmentation to reconstruction and quantification-into a single, accessible software environment.
SN  - 1662-5196
ER  -