Evidence map›Paper›PMID 37867953›Full record

ArticleiScience2023

Deep learning enables stochastic optical reconstruction microscopy-like superresolution image reconstruction from conventional microscopy.

Lei Xu, Shichao Kan, Xiying Yu, Ye Liu, Yuxia Fu, Yiqiang Peng, Yanhui Liang, Yigang Cen, Changjun Zhu, Wei Jiang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

10 authors.

Lei XuDepartment of Etiology and Carcinogenesis and State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Shichao KanSchool of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.
Xiying YuDepartment of Etiology and Carcinogenesis and State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Ye LiuHAMD (Ningbo) Intelligent Medical Technology Co., Ltd, Ningbo 315194, China.
Yuxia FuDepartment of Etiology and Carcinogenesis and State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Yiqiang PengHAMD (Ningbo) Intelligent Medical Technology Co., Ltd, Ningbo 315194, China.
Yanhui LiangHAMD (Ningbo) Intelligent Medical Technology Co., Ltd, Ningbo 315194, China.
Yigang CenInstitute of Information Science, Beijing Jiaotong University, Beijing 100044, China.
Changjun ZhuKey Laboratory of Molecular and Cellular Systems Biology, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China.
Wei JiangDepartment of Etiology and Carcinogenesis and State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite its remarkable potential for transforming low-resolution images, deep learning faces significant challenges in achieving high-quality superresolution microscopy imaging from wide-field (conventional) microscopy. Here, we present X-Microscopy, a computational tool comprising two deep learning subnets, UR-Net-8 and X-Net, which enables STORM-like superresolution microscopy image reconstruction from wide-field images with input-size flexibility. X-Microscopy was trained using samples of various subcellular structures, including cytoskeletal filaments, dot-like, beehive-like, and nanocluster-like structures, to generate prediction models capable of producing images of comparable quality to STORM-like images. In addition to enabling multicolour superresolution image reconstructions, X-Microscopy also facilitates superresolution image reconstruction from different conventional microscopic systems. The capabilities of X-Microscopy offer promising prospects for making superresolution microscopy accessible to a broader range of users, going beyond the confines of well-equipped laboratories.

Indexed as

Machine learningMedical imagingOptical imaging

Identifiers

PMID37867953
PMCPMC10587619

What OpenQuestion holds

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Registered trials

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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.