Evidence map›Paper›PMID 34858975›Full record

ReviewFrontiers in cell and developmental biology2021

Navigating the Light-Sheet Image Analysis Software Landscape: Concepts for Driving Cohesion From Data Acquisition to Analysis.

Holly C Gibbs, Sakina M Mota, Nathan A Hart, Sun Won Min, Alex O Vernino, Anna L Pritchard, Anindito Sen, Stan Vitha, Sreeja Sarasamma, Avery L McIntosh and 5 more

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.2field-weighted citation impact, top 8% of its field
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

10 citing papers in PubMed, 19 citations in OpenAlex.

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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

15 authors at 4 institutions in 1 country.

Holly C GibbsDepartment of Biomedical Engineering, Texas A&M University, College Station, TX, United States.
Sakina M MotaDepartment of Biomedical Engineering, Texas A&M University, College Station, TX, United States.
Nathan A HartDepartment of Biomedical Engineering, Texas A&M University, College Station, TX, United States.
Sun Won MinDepartment of Biology, Texas A&M University, College Station, TX, United States.
Alex O VerninoDepartment of Biology, Texas A&M University, College Station, TX, United States.
Anna L PritchardDepartment of Biomedical Engineering, Texas A&M University, College Station, TX, United States.
Anindito SenMicroscopy and Imaging Center, Texas A&M University, College Station, TX, United States.
Stan VithaMicroscopy and Imaging Center, Texas A&M University, College Station, TX, United States.
Sreeja SarasammaDepartment of Neurology, Baylor College of Medicine, Houston, TX, United States.
Avery L McIntoshMicroscopy and Imaging Center, Texas A&M University, College Station, TX, United States.
Alvin T YehDepartment of Biomedical Engineering, Texas A&M University, College Station, TX, United States.
Arne C LekvenDepartment of Biology and Biochemistry, University of Houston, Houston, TX, United States.
Dylan A McCreedyDepartment of Biomedical Engineering, Texas A&M University, College Station, TX, United States.
Kristen C MaitlandDepartment of Biomedical Engineering, Texas A&M University, College Station, TX, United States.
Lisa M PerezHigh Performance Research Computing, Texas A&M University, College Station, TX, United States.
Texas A&M University · USBaylor College of Medicine · USImaging Center · USUniversity of Houston · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

From the combined perspective of biologists, microscope instrumentation developers, imaging core facility scientists, and high performance computing experts, we discuss the challenges faced when selecting imaging and analysis tools in the field of light-sheet microscopy. Our goal is to provide a contextual framework of basic computing concepts that cell and developmental biologists can refer to when mapping the peculiarities of different light-sheet data to specific existing computing environments and image analysis pipelines. We provide our perspective on efficient processes for tool selection and review current hardware and software commonly used in light-sheet image analysis, as well as discuss what ideal tools for the future may look like.

Indexed as

image analysislight-sheetmultiview deconvolutionparallel processingtool selection

Identifiers

PMID34858975
PMCPMC8631767
OpenAlexW3208470271

What OpenQuestion holds

Textmetadata
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.