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

Authors

Ying Hu, Xiaojun Wang, Pei Sun, Yuan Shen, Tingwei Quan

Published in

Frontiers in neuroinformatics. Volume 20. Pages 1925112. Epub Oct 07, 2026.

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.

PMID:
42908880
Bibliographic data and abstract were imported from PubMed on 23 Oct 2026.

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