Fast and robust multiplane single molecule localization microscopy using deep neural network
This addresses the challenge of robust 3D localization in biological imaging, though it appears incremental as it builds on existing compressed sensing and deep learning approaches.
The study tackled the problem of 3D single molecule localization accuracy being degraded by lateral camera drifts in multifocal plane microscopy, and the result was a method achieving 20 nm lateral and 50 nm axial accuracy without explicit drift correction.
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.