cellSTORM - Cost-effective Super-Resolution on a Cellphone using dSTORM

TL;DR

This paper demonstrates that consumer smartphones can perform super-resolution microscopy using dSTORM, achieving sub-80 nm resolution, and introduces a GAN-based reconstruction method that enables processing directly on the device.

Contribution

It shows that smartphones can be used for super-resolution microscopy and introduces a GAN-based reconstruction approach for improved imaging performance.

Findings

Achieved <80 nm resolution with a smartphone camera.

Developed a GAN-based reconstruction method for super-resolution imaging.

Enabled processing directly on the smartphone device.

Abstract

Expensive scientific camera hardware is amongst the main cost factors in modern, high-performance microscopes. Recent technological advantages have, however, yielded consumer-grade camera devices that can provide surprisingly good performance. The camera sensors of smartphones in particular have benefited of this development. Combined with computing power and due to their ubiquity, smartphones provide a fantastic opportunity for "imaging on a budget". Here we show that a consumer cellphone is capable even of optical super-resolution imaging by (direct) Stochastic Optical Reconstruction Microscopy (dSTORM), achieving optical resolution better than 80 nm. In addition to the use of standard reconstruction algorithms, we investigated an approach by a trained image-to-image generative adversarial network (GAN). This not only serves as a versatile technique to reconstruct video sequences under conditions where traditional algorithms provide sub-optimal localization performance, but also allows processing directly on the smartphone. We believe that "cellSTORM" paves the way for affordable super-resolution microscopy suitable for research and education, expanding access to cutting edge research to a large community.