{
  "abstract": "High-throughput 3D microscopic imaging generates brain-volume datasets at terabyte to petabyte scale, posing significant storage challenges for large-scale neuroimaging data. In this study, we introduce NeuroPack, a hyperprior-driven 3D deep-learning-based packaging toolkit specifically designed for compressing 3D microscopic volumes at the mesoscale. NeuroPack encodes a compact hyperprior stream that conditions its entropy model on local voxel-wise variance, adaptively allocating bits to preserve mesoscale anatomical details for an optimal rate-distortion balance. To ensure reproducible deployment across heterogeneous microscopy datasets and computing environments, NeuroPack implements model integerization for the hyperprior side-information pathway, whose hyper-decoder predicts the Gaussian mean and scale parameters used by the arithmetic coder. By replacing platform-dependent floating-point hyper-decoding with deterministic fixed-point integer convolutions, NeuroPack produces bit-exact entropy models across CPUs, GPUs, local workstations, and cloud servers, thereby preventing decoding drift caused by floating-point rounding, fused-multiply-add ordering, or library-specific kernels. This integerized design preserves rate-distortion behavior while maintaining efficient encoding and decoding for large anisotropic 3D microscopy volumes. Our method enables high-fidelity microscopy reconstruction at 100- to 1000-fold compression across diverse modalities and species. Quantitatively, NeuroPack delivers the strongest overall rate-distortion performance in both BD-PSNR and BD-MSSSIM, with average relative gains of 73.4% and 50.8%, respectively, over the best competing codec. Importantly, NeuroPack preserves image fidelity required for downstream morphological reconstruction and analysis, as validated by simultaneous human annotation of original and decompressed confocal whole-brain zebrafish microscopy images. Together, NeuroPack provides a learning-based compression framework explicitly tailored for microscopy data, establishing a scalable storage solution for data-intensive neuroimaging and AI-empowered biological discovery.",
  "abstract_shown": true,
  "affiliations": [
    "Shanghai Artificial Intelligence Laboratory"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "name": "R. Zhao"
    },
    {
      "name": "X. Yang"
    },
    {
      "name": "J. Wu"
    },
    {
      "name": "Q. Zheng"
    },
    {
      "name": "J. Zhang"
    },
    {
      "name": "J.-Q. Guan"
    },
    {
      "name": "W. Cai"
    },
    {
      "name": "J.-L. Du"
    },
    {
      "name": "L. Bai"
    },
    {
      "name": "X.-F. Du"
    },
    {
      "name": "W. Ouyang"
    },
    {
      "name": "G. Lu"
    },
    {
      "affs": [
        0
      ],
      "name": "C. Song"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b7hzq34btyb7f.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.29.762731",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.29.762731v1.full",
  "id": "b7hzq34btyb7f",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.29.762731v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioengineering"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.29.762731v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b7hzq34btyb7f.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:21:07Z",
      "native_id": "10.64898/2026.10.29.762731",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.29.762731v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioengineering",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "NeuroPack: Advanced Learnable Packaging Toolkit Towards Ultra-High Compression Ratio for Volumetric Neuroimaging",
  "title_text": "NeuroPack: Advanced Learnable Packaging Toolkit Towards Ultra-High Compression Ratio for Volumetric Neuroimaging",
  "type": "preprint",
  "updated_at": "2026-11-01T00:20:48Z",
  "url": "https://research.draconitic.com/paper/b7hzq34btyb7f",
  "venue": "bioRxiv",
  "versions": []
}