Deep-STORM: Super-resolution Single-molecule Microscopy by Deep Learning

In conventional microscopy, the spatial resolution of an image is bounded by Abbe’s diffraction limit, corresponding to approximately half the optical wavelength. Localization microscopy methods, e.g. PALM and STORM have revolutionized biological imaging in the last decade, enabling the observation of cellular structures at the nanoscale. In single-molecule localization microscopy, regions with a high density of overlapping emitters pose an algorithmic challenge. While successful localization of densely-spaced emitters has been demonstrated, all existing methods suffer from two fundamental drawbacks: long data-processing time and sample-dependent parameter tuning. Here, we demonstrate super-resolution image reconstruction by harnessing Deep-Learning.

Our method, Deep-STORM, does not explicitly localize emitters. Instead, it creates a super-resolved image from the raw data directly. The net produces images with reconstruction resolution comparable or better than existing methods; furthermore, the method is extremely fast, and our software can leverage GPU computation for further enhanced speed. Moreover, Deep-STORM is parameter free, requiring no expertise from the user, and importantly, Deep-STORM is general and does not rely on any prior knowledge of the structure in the sample, making the method applicable to any single-molecule blinking dataset.

We validated Deep-STORM on both simulated and experimental data of biological structures. Deep-STORM not only yields image reconstruction results that are comparable to or better than leading algorithms, but also does so ~1-3 orders of magnitude faster. When introducing GPU acceleration, Deep-STORM reconstruction is equivalent to localizing ~20000 emitters per second, compared to ~1500 emitters per second by the fastest existing multi-emitter fitting method to our knowledge. To conclude, Deep-STORM combines state-of-the-art resolution enhancement, unprecedented speed, and high flexibility (parameter-free operation). This combination produces a technique capable of video-rate analysis of super-resolution localization-microscopy data that requires no expertise from the end user, overcoming some of the most significant limitations of existing localization methods.