Evidence map›Paper›PMID 33408995›Full record

ArticleBiomedical optics express2020

Deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM) for easy isotropic volumetric imaging of large biological specimens.

Fang Zhao, Lanxin Zhu, Chunyu Fang, Tingting Yu, Dan Zhu, Peng Fei

Abstract read
In one paragraph

Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Review
  7. Review
  8. Article
  9. Roadmap on Label-Free Super-Resolution Imaging.Laser & photonics reviews · 2023
    Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. Review
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

6 authors.

Fang ZhaoSchool of Optical and Electronic Information- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
Lanxin ZhuSchool of Optical and Electronic Information- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
Chunyu FangSchool of Optical and Electronic Information- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
Tingting YuBritton Chance center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
Dan ZhuBritton Chance center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
Peng FeiSchool of Optical and Electronic Information- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Isotropic 3D histological imaging of large biological specimens is highly desired but remains highly challenging to current fluorescence microscopy technique. Here we present a new method, termed deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM), to enable fast, isotropic light-sheet fluorescence imaging on a conventional wide-field microscope. After integrating a minimized add-on device that transforms an inverted microscope into a 3D light-sheet microscope, we further integrate a deep neural network (DNN) procedure to quickly restore the ambiguous z-reconstructed planes that suffer from still insufficient axial resolution of light-sheet illumination, thereby achieving isotropic 3D imaging of thick biological specimens at single-cell resolution. We apply this easy and cost-effective Deep-SLAM approach to the anatomical imaging of single neurons in a meso-scale mouse brain, demonstrating its potential for readily converting commonly-used commercialized 2D microscopes to high-throughput 3D imaging, which is previously exclusive for high-end microscopy implementations.

Identifiers

PMID33408995
PMCPMC7747920

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.