Fig. 5: Super-resolution microscopy network (Deep-STORM).
From: Democratising deep learning for microscopy with ZeroCostDL4Mic
Example of data that can be generated using the ZeroCostDL4Mic Deep-STORM notebook. a Single frame of the raw BIN10 dataset, phalloidin labelling of a glial cell, and the wide-field image. b Top: Comparison of ThunderSTORM51 Multi-Emitter Maximum likelihood estimation (ME-MLE) and Deep-STORM reconstructions (see also Supplementary Movie 10). ME-MLE processing times were estimated using an Intel Core i7-8700 CPU @ 3.2 GHz, 64GB RAM machine. Bottom: SQUIRREL52 analysis comparing reconstructions from ThunderSTORM ME-MLE and Deep-STORM, highlighting better linearity of the reconstruction with respect to the equivalent wide-field dataset for Deep-STORM. c Single frame of the raw of a DNA-PAINT50 dataset of a U2OS cell immuno-labelled for tubulin and the wide-field image. d Top: Comparison of ThunderSTORM51 Multi-Emitter Maximum likelihood estimation (ME-MLE) and Deep-STORM reconstructions. ME-MLE processing times were estimated using an Intel Core i7-8700 CPU @ 3.2 GHz, 32 GB RAM. Bottom: SQUIRREL52 analysis comparing reconstructions of ThunderSTORM ME-MLE and Deep-STORM, highlighting better linearity of the reconstruction with respect to the equivalent wide-field dataset for Deep-STORM. The reconstruction times shown for Deep-STORM were obtained with the NVIDIA Tesla P100 PCIe 16 GB RAM available on Google Colab. RSP resolution -scaled Pearson coefficient, RSE root-squared error.
