Evidence map›Paper›PMID 40355726›Full record

ArticleNature methods2025

Dark-based optical sectioning assists background removal in fluorescence microscopy.

Ruijie Cao, Yaning Li, Yao Zhou, Meiqi Li, Fangrui Lin, Wenyi Wang, Guoxun Zhang, Gang Wang, Boya Jin, Wei Ren and 19 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. Time-deterministic cryo-optical microscopy.Light, science & applications · 2025
    Article
  8. Article
  9. Voltage Imaging with Periodic Structured Illumination.bioRxiv : the preprint server for biology · 2025
    Article
  10. Article
  11. 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

29 authors.

Ruijie Cao *Department of Biomedical Engineering, National Biomedical Imaging Center, Peking University, College of Future Technology, Beijing, China.ORCID http://orcid.org/0000-0002-2463-1136
Yaning Li *Department of Biomedical Engineering, National Biomedical Imaging Center, Peking University, College of Future Technology, Beijing, China.
Yao ZhouSchool of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.ORCID http://orcid.org/0000-0003-3385-3686
Meiqi LiSchool of Life Sciences, Peking University, Beijing, China.ORCID http://orcid.org/0000-0003-3586-176X
Fangrui LinState Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China.
Wenyi WangDepartment of Biomedical Engineering, National Biomedical Imaging Center, Peking University, College of Future Technology, Beijing, China.ORCID http://orcid.org/0000-0002-9843-7424
Guoxun ZhangDepartment of Automation, Institute for Brain and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China.
Gang WangAiry Technologies Co., Beijing, China.
Boya JinDepartment of Biomedical Engineering, National Biomedical Imaging Center, Peking University, College of Future Technology, Beijing, China.
Wei RenDepartment of Biomedical Engineering, National Biomedical Imaging Center, Peking University, College of Future Technology, Beijing, China.
Yu SunDepartment of Biomedical Engineering, National Biomedical Imaging Center, Peking University, College of Future Technology, Beijing, China.
Zhifeng ZhaoDepartment of Automation, Institute for Brain and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China.
Wei ZhangState Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China.
Jing SunKey Laboratory of Analytical Science and Technology of Hebei Province, College of Chemistry and Materials Science, Hebei University, Hebei, China.
Yiwei HouDepartment of Biomedical Engineering, National Biomedical Imaging Center, Peking University, College of Future Technology, Beijing, China.ORCID http://orcid.org/0000-0002-5764-2384
Xinzhu XuDepartment of Biomedical Engineering, National Biomedical Imaging Center, Peking University, College of Future Technology, Beijing, China.ORCID http://orcid.org/0000-0003-2302-2053
Jiakui HuDepartment of Biomedical Engineering, National Biomedical Imaging Center, Peking University, College of Future Technology, Beijing, China.
Wei ShiDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.
Shuang FuDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.ORCID http://orcid.org/0000-0003-0132-5707
Qianxi LiangDepartment of Biomedical Engineering, National Biomedical Imaging Center, Peking University, College of Future Technology, Beijing, China.
Yanye LuDepartment of Biomedical Engineering, National Biomedical Imaging Center, Peking University, College of Future Technology, Beijing, China.
Changhui LiDepartment of Biomedical Engineering, National Biomedical Imaging Center, Peking University, College of Future Technology, Beijing, China.ORCID http://orcid.org/0000-0002-6179-2684
Yuxuan ZhaoSchool of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.
Yiming LiDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.ORCID http://orcid.org/0000-0003-4109-3824
Dong KuangInstitute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Jiamin WuDepartment of Automation, Institute for Brain and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China.ORCID http://orcid.org/0000-0003-3479-1026
Peng FeiSchool of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.ORCID http://orcid.org/0000-0003-3764-817X
Junle QuState Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China. jlqu@szu.edu.cn.ORCID http://orcid.org/0000-0001-7833-4711
Peng XiDepartment of Biomedical Engineering, National Biomedical Imaging Center, Peking University, College of Future Technology, Beijing, China. xipeng@pku.edu.cn.ORCID http://orcid.org/0000-0001-6626-4840

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62025501National Natural Science Foundation of China (National Science Foundation of China) 62305004National Natural Science Foundation of China (National Science Foundation of China) 62335008National Natural Science Foundation of China (National Science Foundation of China) 62405010National Natural Science Foundation of China (National Science Foundation of China) 624B2009National Natural Science Foundation of China (National Science Foundation of China) 92150301National Natural Science Foundation of China (National Science Foundation of China) T2421003
6 · The paper itself

Abstract

In fluorescence microscopy, a persistent challenge is the defocused background that obscures cellular details and introduces artifacts. Here, we introduce Dark sectioning, a method inspired by natural image dehazing for removing backgrounds that leverages dark channel prior and dual frequency separation to provide single-frame optical sectioning. Unlike denoising or deconvolution, Dark sectioning specifically targets and removes out-of-focus backgrounds, stably improving the signal-to-background ratio by nearly 10 dB and structural similarity index measure of images by approximately tenfold. Dark sectioning was validated using wide-field, confocal, two/three-dimensional structured illumination and one/two-photon microscopy with high-fidelity reconstruction. We further demonstrate its potential to improve the segmentation accuracy in deep tissues, resulting in better recognition of neurons in the mouse brain and accurate assessment of nuclei in prostate lesions or mouse brain sections. Dark sectioning is compatible with many other microscopy modalities, including light-sheet and light-field microscopy, as well as processing algorithms, including deconvolution and super-resolution optical fluctuation imaging.

Indexed as

Image Processing, Computer-AssistedAlgorithmsAnimalsBrainHumansImaging, Three-DimensionalMaleMiceMicroscopy, FluorescenceNeuronsProstateSignal-To-Noise Ratio

Identifiers

PMID40355726

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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.