3-D super-resolution localization microscopy using deep-learning method

Abstract

Super-resolution localization microscopy (SRLM) techniques overcome the diffraction limit, making possible the observation of sub-cellular structures in vivo. At present, the spatial resolution of ~20 nm in x-y axis has been achieved in SRLM. However, the localization accuracy for the longitudinal axis (i.e., the z-axis) still need be improved. Although some methods have been proposed to implement 3-D SRLM, these methods are computationally intensive and parameter dependent. To overcome these limitations, in this paper, we propose a new method based on deep learning, termed as dl- 3D-SRLM. By learning the mapping between a 2-D camera frame (i.e., the experimentally acquired image) and the true 3-D locations of fluorophores in the corresponding image region with a convolutional neural network (CNN), dl-3D-SRLM provides the possibility of implementing 3-D SRLM with a high localization accuracy, a fast data-processing speed, and a little human intervention. To evaluate the performance of dl-3D-SRLM, a series of numerical simulations are performed. The results show that when using dl-3D-SRLM, we can accurately resolve the 3-D location of fluorophores from the acquired 2-D images, even if under high fluorophores densities and low signal-to-noise ratio conditions. In addition, the complex 3-D structure can also be effectively imaged by dl-3D-SRLM. As a result, dl-3D-SRLM is more beneficial for 3D-SRLM imaging.