| Autor: |
Jia Zhe, Zhou Lingxiao, Li Haoyu, Ni Jielei, Chen Danni, Guo Dongfei, Cao Bo, Liu Gang, Liang Guotao, Zhou Qianwen, Yuan Xiaocong, Ni Yanxiang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Nanophotonics, Vol 13, Iss 19, Pp 3647-3661 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2192-8614 |
| DOI: |
10.1515/nanoph-2023-0936 |
| Popis: |
Precisely pinpointing the positions of emitters within the diffraction limit is crucial for quantitative analysis or molecular mechanism investigation in biomedical research but has remained challenging unless exploiting single molecule localization microscopy (SMLM). Via integrating experimental spot dataset with deep learning, we develop a new approach, Digital-SMLM, to accurately predict emitter numbers and positions for sub-diffraction-limit spots with an accuracy of up to 98 % and a root mean square error as low as 14 nm. Digital-SMLM can accurately resolve two emitters at a close distance, e.g. 30 nm. Digital-SMLM outperforms Deep-STORM in predicting emitter numbers and positions for sub-diffraction-limited spots and recovering the ground truth distribution of molecules of interest. We have validated the generalization capability of Digital-SMLM using independent experimental data. Furthermore, Digital-SMLM complements SMLM by providing more accurate event number and precise emitter positions, enabling SMLM to closely approximate the natural state of high-density cellular structures. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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