{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "School of Mathematics and Statistics, Hubei University of Education, Wuhan, Hubei, China.",
    "Institute of Big Data Analysis and Applied Mathematics, Hubei University of Education, Wuhan, Hubei, China.",
    "Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya, China.",
    "Department of Clinical Research Institute, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China.",
    "Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Ying Hu"
    },
    {
      "affs": [
        2
      ],
      "name": "Xiaojun Wang"
    },
    {
      "affs": [
        3
      ],
      "name": "Pei Sun"
    },
    {
      "affs": [
        4
      ],
      "name": "Yuan Shen"
    },
    {
      "affs": [
        4
      ],
      "name": "Tingwei Quan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6txbkq5j7r2hd.bib",
  "cited_by": 0,
  "doi": "10.3389/fninf.2026.1925112",
  "fulltext_url": "https://europepmc.org/articles/PMC13683564",
  "id": "6txbkq5j7r2hd",
  "issns": [
    "1662-5196"
  ],
  "landing_url": "https://europepmc.org/article/MED/42908880",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1304|Cell Image Analysis Techniques"
    ]
  ],
  "openalex": "W7220846074",
  "pdf_url": "https://europepmc.org/articles/PMC13683564?pdf=render",
  "pmcid": "PMC13683564",
  "pmid": "42908880",
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6txbkq5j7r2hd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:14Z",
      "native_id": "MED:42908880",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42908880"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1304|Cell Image Analysis Techniques",
      "subfield": "biophysics"
    }
  ],
  "title": "BVLab-analyzer: a unified visual toolbox for terabyte-scale whole-brain cell detection, neuron reconstruction, and vascular network extraction.",
  "title_text": "BVLab-analyzer: a unified visual toolbox for terabyte-scale whole-brain cell detection, neuron reconstruction, and vascular network extraction.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:14Z",
  "url": "https://research.draconitic.com/paper/6txbkq5j7r2hd",
  "venue": "Frontiers in neuroinformatics",
  "versions": []
}