napari gets slow for large images

[constantinpape]

Another possible improvement - if the label layers created by micro-sam ("current" and "commited") are entirely in-memory numpy arrays, we might get a performance benefit from using something like zarr instead.

Apologies if this suggestion is already something that happens, or incompatible with the code. I have not looked at this part of the code in detail yet.

I've noticed that editing (drawing/erasing) the labels takes a very long time for very large 2D images. I have not tried in 3D, but I guess something similar happens there.

Originally posted by @GenevieveBuckley in https://github.com/computational-cell-analytics/micro-sam/issues/34#issuecomment-1577142836

[psobolewskiPhD]

Something I've toyed with is using tensorstore transactions and loading and writing just patches (using commit). Here's a gist example: https://gist.github.com/psobolewskiPhD/0ca0bc2d16882cf83d2921512a70962e