ArticleOptics express2022
Deep-SMOLM: deep learning resolves the 3D orientations and 2D positions of overlapping single molecules with optimal nanoscale resolution.
Article in Optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Single Fluorogens and Orientation-Localization Microscopy for Quantifying Chemical and Biomolecular Dynamics at the Nanoscale.Accounts of chemical research · 2026Article
- Fused deep learning enables 6D single-molecule localization in polarization-resolved microscopy.Methods and applications in fluorescence · 2026Article
- SIMBA: an agentic AI platform for single-molecule multidimensional imaging.bioRxiv : the preprint server for biology · 2026Article
- 4polar3D single molecule imaging of 3D orientation in dense actin networks using ratiometric polarization splitting.Nature communications · 2026Article
- Single molecule orientation and localization microscopy.Nature photonics · 2025Article
- Painting rich six-dimensional pictures using polarized fluorescence microscopy.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Single-molecule orientation-localization microscopy: Applications and approaches.Quarterly reviews of biophysics · 2024Review
- Resolving the Nanoscale Structure of β-Sheet Peptide Self-Assemblies Using Single-Molecule Orientation-Localization Microscopy.ACS nano · 2024Article
- Combining deep learning approaches and point spread function engineering for simultaneous 3D position and 3D orientation measurements of fluorescent single molecules.Optics communications · 2023Article
- Deep-SMOLM: deep learning resolves the 3D orientations and 2D positions of overlapping single molecules with optimal nanoscale resolution.Optics express · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Dipole-spread function (DSF) engineering reshapes the images of a microscope to maximize the sensitivity of measuring the 3D orientations of dipole-like emitters. However, severe Poisson shot noise, overlapping images, and simultaneously fitting high-dimensional information-both orientation and position-greatly complicates image analysis in single-molecule orientation-localization microscopy (SMOLM). Here, we report a deep-learning based estimator, termed Deep-SMOLM, that achieves superior 3D orientation and 2D position measurement precision within 3% of the theoretical limit (3.8° orientation, 0.32 sr wobble angle, and 8.5 nm lateral position using 1000 detected photons). Deep-SMOLM also demonstrates state-of-art estimation performance on overlapping images of emitters, e.g., a 0.95 Jaccard index for emitters separated by 139 nm, corresponding to a 43% image overlap. Deep-SMOLM accurately and precisely reconstructs 5D information of both simulated biological fibers and experimental amyloid fibrils from images containing highly overlapped DSFs at a speed ~10 times faster than iterative estimators.
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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.