Evidence map›Paper›PMID 31686039›Full record

ArticleNature methods2019

Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning.

Yichen Wu, Yair Rivenson, Hongda Wang, Yilin Luo, Eyal Ben-David, Laurent A Bentolila, Christian Pritz, Aydogan Ozcan

Abstract read
PubMed Publisher
In one paragraph

Article in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers.

0numbers the graph read from it
0cells of the map it votes in
88citing 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

88 citing papers in PubMed.

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28 more citing papers are in PubMed but not listed here.

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

8 authors.

Yichen Wu *Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-9343-5489
Yair Rivenson *Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-1132-0715
Hongda WangElectrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-0110-5624
Yilin LuoElectrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-4611-3049
Eyal Ben-DavidDepartment of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Laurent A BentolilaCalifornia Nano Systems Institute (CNSI), University of California, Los Angeles, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-8758-1594
Christian PritzDepartment of Genetics, Hebrew University of Jerusalem, Jerusalem, Israel.
Aydogan OzcanElectrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA. ozcan@ucla.edu.ORCID http://orcid.org/0000-0002-0717-683X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We demonstrate that a deep neural network can be trained to virtually refocus a two-dimensional fluorescence image onto user-defined three-dimensional (3D) surfaces within the sample. Using this method, termed Deep-Z, we imaged the neuronal activity of a Caenorhabditis elegans worm in 3D using a time sequence of fluorescence images acquired at a single focal plane, digitally increasing the depth-of-field by 20-fold without any axial scanning, additional hardware or a trade-off of imaging resolution and speed. Furthermore, we demonstrate that this approach can correct for sample drift, tilt and other aberrations, all digitally performed after the acquisition of a single fluorescence image. This framework also cross-connects different imaging modalities to each other, enabling 3D refocusing of a single wide-field fluorescence image to match confocal microscopy images acquired at different sample planes. Deep-Z has the potential to improve volumetric imaging speed while reducing challenges relating to sample drift, aberration and defocusing that are associated with standard 3D fluorescence microscopy.

Indexed as

Deep LearningAnimalsCaenorhabditis elegansMicroscopy, ConfocalMicroscopy, FluorescenceNeurons

Identifiers

PMID31686039

What OpenQuestion holds

Textmetadata
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.