High-axial-resolution single-molecule localization under dense excitation with a multi-channel deep U-Net

Weihang Zhang, Zhihong Zhang, Liheng Bian, Haoqian Wang, Jinli Suo*, Qionghai Dai

*此作品的通讯作者

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2 引用 (Scopus)

摘要

Single-molecule localization microscopy (SMLM) can bypass the diffraction limit of optical microscopes and greatly improve the resolution in fluorescence microscopy. By introducing the point spread function (PSF) engineering technique, we can customize depth varying PSF to achieve higher axial resolution. However, most existing 3D single-molecule localization algorithms require excited fluorescent molecules to be sparse and captured at high signal-to-noise ratios, which results in a long acquisition time and precludes SMLM’s further applications in many potential fields. To address this problem, we propose a novel 3D single-molecular localization method based on a multi-channel neural network based on U-Net. By leveraging the deep network’s great advantages in feature extraction, the proposed network can reliably discriminate dense fluorescent molecules with overlapped PSFs and corrupted by sensor noise. Both simulated and real experiments demonstrate its superior performance in PSF engineered microscopes with short exposure and dense excitations, which holds great potential in fast 3D super-resolution microscopy.

源语言英语
页(从-至)5477-5480
页数4
期刊Optics Letters
46
21
DOI
出版状态已出版 - 1 11月 2021

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