Gen-AI producing fancy data augmentation for Super-resolution microscopy! Great work @AlonSaguy and team! @HenriquesLab @HeilemannLab.

Great work @yevgenin @DanielleSapir et al.!.

We used the Incucyte system by @SartoriusGlobal. All this and more in our latest paper: Big thanks and congrats to all co-authors and collaborators: Nadav, @EliasNehme9 Noam, Ilana, Reut, Boris, Paul, @AlaloufOnit , @SartoriusGlobal !.

This enables PSF engineering in space-limited microscopy systems, e.g. that fit into incubators. Uses include extended depth-of-field and snapshot 3D spheroid from 2D, using algorithms by @EliasNehme9. Bonus: Obtaining training images is easy with a high-throughput microscope.

PSF engineering typically requires a 4-f system, but this is not always feasible, e.g. due to size constraints. Nadav Opatovski attached a phase mask directly to the objective lens, see beads defocusing here. Tetrapod mask shown here (wait for the end):

🥳 DBlink is now peer reviewed and improved! Online in @naturemethods: Congrats @AlonSaguy, @AlaloufOnit, @HeilemannLab et al.!.

More info in the preprint: …, where we demonstrate the use-case of generating training data for CARE (by.@martweig et al.).3/4.

This is a very ongoing project, with many open questions: what are the performance limits? How much can we really gain beyond the information in the training data? and of course: what would be the best uses of something like this in super-resolution microscopy?.4/4.

Alon Saguy et al. trained a diffusion model to generate synthetic images of super-resolution microscopy images using data of only a few images from ShareLoc (. 2/4.

One of these super-resolution microtubule images was captured by a microscope and the other one was generated by a diffusion model. Can you tell which is which? Try more here: 1/4

Great work by @yevgenin applying information theory to optical genome mapping, with implications to optimal label design.

RT @LabPresse: Major paper announcement:. Review “Fluorescence Microscopy: a statistics-optics perspective” is now on arxiv. with Fazel, G….

2 bonus points if you can guess what this DOE does (clue in the preprint 🙂)

We generate a variety of solid elements, including microlenses, vortex plates for MINSTED and more, and pretty challenging PSF engineering masks! Thanks to our collaborating groups, Ady Arie, @Stefan_W_Hell @AleksPonjavic, in this multi-national effort led by Reut Kedem Orange!.

Computational microscopy often relies on some kind of wavefront shaping by diffractive optical components, which can be very difficult to fabricate. This motivated us to utilize 3D printing + near index matching for simple & cheap DOE fabrication.

Just out: Ofri Goldenberg learned and implemented a multicolor phase mask + decoding net for determining the distance between a pair of 2-colored spots in 3D. The net outputs the scalar distance of interest.

Consider the problem of determining the 3D distance between a pair of 2-colored spots. One option is to find x1,y1,z1 and x2,y2,z2, e.g. using a 3D PSF, and calculate the distance. But why not optically encode the *distance*, rather than individual positions?.

In many (most?) cases in fluorescence microscopy, in which the positions of spots are determined, it is not really their global positions that are of interest, but rather their *relative* positions, i.e. their distances from one another.

cool idea + implemented using simple elements!.

Phase mask is a double-helix, and for image analysis we used @SjoerdStallinga's excellent cepstrum method or a neural net by Dafei Xiao. More info in the paper + realization how fortunate we are in microscopy to neglect atmospheric turbulence + an excuse for us to buy a drone.