.png){kind=icon}
.png){kind=icon}
Return
Deep-SMOLM: deep learning resolves the 3D orientations and 2D positions of overlapping single molecules with optimal nanoscale resolution
.png){kind=icon}
OA
AI
DOI：10.1364/OE.470146.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 degrees 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 similar to 10 times faster than iterative estimators.(c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Keywords:
POINT-SPREAD FUNCTION
FLUORESCENCE MICROSCOPY
LOCALIZATION MICROSCOPY
ACCURATE
IMAGE
Journal
IF:
3.3
Papers:
6.1W
Citations:
14.3W