Evidence map›Paper›PMID 39420143›Full record

ArticleNature methods2024

Image processing tools for petabyte-scale light sheet microscopy data.

Xiongtao Ruan, Matthew Mueller, Gaoxiang Liu, Frederik Görlitz, Tian-Ming Fu, Daniel E Milkie, Joshua L Lillvis, Alexander Kuhn, Johnny Gan Chong, Jason Li Hong and 6 more

Abstract read
In one paragraph

Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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  5. Article
  6. Polarization-engineered aberration-resilient light sheet microscopy.bioRxiv : the preprint server for biology · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. A Pandemic-Scale Ancestral Recombination Graph for SARS-CoV-2.bioRxiv : the preprint server for biology · 2025
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
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  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Xiongtao RuanDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, US. xruan@berkeley.edu.ORCID http://orcid.org/0000-0001-5322-3249
Matthew MuellerDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, US.ORCID http://orcid.org/0000-0003-0774-1031
Gaoxiang LiuDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, US.
Frederik GörlitzDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, US.
Tian-Ming FuJanelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, US.ORCID http://orcid.org/0000-0001-6265-0859
Daniel E MilkieJanelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, US.ORCID http://orcid.org/0000-0002-3917-6965
Joshua L LillvisJanelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, US.
Alexander KuhnNVIDIA, Berlin, Germany.
Johnny Gan ChongDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, US.ORCID http://orcid.org/0000-0001-8925-6368
Jason Li HongDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, US.ORCID http://orcid.org/0009-0009-3419-808X
Chu Yi Aaron HerrDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, US.
Wilmene HerculeDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, US.
Marc NienhausNVIDIA, Berlin, Germany.
Alison N KillileaDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, US.ORCID http://orcid.org/0000-0002-8308-8196
Eric Betzig *Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, US. betzige@janelia.hhmi.org.ORCID http://orcid.org/0000-0002-4192-1274
Srigokul Upadhyayula *Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, US. sup@berkeley.edu.ORCID http://orcid.org/0000-0001-6727-5883

Funding

Alexander von Humboldt-Stiftung (Alexander von Humboldt Foundation) Feodor Lynen Research FellowshipCalifornia Institute for Regenerative Medicine (CIRM) EDUC4-12790DOE | LDRD | Lawrence Berkeley National Laboratory (Berkeley Lab) 7647437DOE | LDRD | Lawrence Berkeley National Laboratory (Berkeley Lab) 7721359
6 · The paper itself

Abstract

Light sheet microscopy is a powerful technique for high-speed three-dimensional imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are optimized for memory and performance. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson-Lucy deconvolution and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel rates of modern imaging cameras. The software opens new avenues for biological discoveries through large-scale imaging experiments.

Indexed as

Image Processing, Computer-AssistedSoftwareAlgorithmsAnimalsHumansImaging, Three-DimensionalMicroscopy

Identifiers

PMID39420143
PMCPMC11621031

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.