Fast and robust multiplane single molecule localization microscopy using deep neural network

TL;DR

This paper introduces a deep neural network approach for 3D single molecule localization microscopy that is fast, accurate, and robust to lateral drifts, improving measurement precision without explicit drift correction.

Contribution

The study presents a novel deep learning method that jointly estimates 3D molecule positions and lateral drifts, enhancing robustness and accuracy in multifocal plane microscopy.

Findings

Achieves 20 nm lateral and 50 nm axial localization accuracy.

Robust to lateral drifts without explicit correction.

Faster and more accurate than traditional methods.

Abstract

Single molecule localization microscopy is widely used in biological research for measuring the nanostructures of samples smaller than the diffraction limit. This study uses multifocal plane microscopy and addresses the 3D single molecule localization problem, where lateral and axial locations of molecules are estimated. However, when we multifocal plane microscopy is used, the estimation accuracy of 3D localization is easily deteriorated by the small lateral drifts of camera positions. We formulate a 3D molecule localization problem along with the estimation of the lateral drifts as a compressed sensing problem, A deep neural network was applied to accurately and efficiently solve this problem. The proposed method is robust to the lateral drifts and achieves an accuracy of 20 nm laterally and 50 nm axially without an explicit drift correction.