Learning the specimen structure in single-molecule localization microscopy

Single-molecule localization microscopy (SMLM) reconstructs three-dimensional organic specimens stained by fluorophores - molecules that emit light under laser excitation - beyond the diffraction limit by localizing active emitters in thousands of frames and accumulating their positions into a point cloud. However, conventional methods process short windows of frames independently, ignoring that all frames share the same underlying structure. In this paper, we propose a method that learns the structure of the specimen and uses it to improve emitter localization. We train a neural network on simulated data to detect and localize emitters, while simultaneously fitting a distribution over emitters to the localizations it predicts on real frames. We use this distribution to adapt the simulator throughout training, hence feeding information from the whole acquisition back into individual localizations. We jointly estimate the point spread function, the background, and the emitter photophysics via the same procedure, enabling self-calibration. After training, we obtain a network for detecting and localizing emitters, and a fitted spatial density that directly provides a continuous super-resolved representation of the specimen. On synthetic benchmarks, learning the specimen structure reduces localization errors compared with the same network trained with uniformly distributed emitters. On real acquisitions, the fitted density reveals fine specimen structure, showing smooth filaments and clearly defined nuclear pore rings.