Open Nespresso2000 opened 2 months ago
I don't suppose there is any difference between 3D and 2D. Take a look at this link on how to use Cellpose on Dask arrays.
I don't know exactly what you mean by block_size or distributed_eval.
The method mentioned in the above link results in a dask array of labels. You can view separate segmentation chunk results by iterating through them and computing them.
Hello! @Nespresso2000 we just released a package for large-scale segmentation with cellpose (2D/3D images). Still in development but it'd be nice if you give it a shot. Unfortunately, you'd have to convert your data to OMEZarr.
Hello! @Nespresso2000 we just released a package for large-scale segmentation with cellpose (2D/3D images). Still in development but it'd be nice if you give it a shot. Unfortunately, you'd have to convert your data to OMEZarr.
Hi @camilolaiton I am interested in trying this out! How would I go about testing this? I have large datasets for Cellpose segmentation saved as zarr
arrays. Do you have a tutorial on how to run your package in python? Thank you.
Hi @Eddymorphling, not yet. I'll include a notebook with examples this week. In the meantime, you could take a look at this function main. We are using the OME-NGFF convention for the zarr (OME-Zarr) to get the image metadata (e.g., resolution).
Please, let me know if you have following questions. Make sure to properly set the parameters for the machine you're using.
Hi,
I want to use cellpose on some rather large .tif files of sizes ranging from 1GB-10GB. They are 2-d RGB images. I have a Tesla T4 GPU and 32GB RAM. Following #356 and #244 I first used openslide to convert my image and write them on my disk as zarr arrays; so now, I have a folder of zarr arrays corresponding to one image. To be honest, I am not able to understand exactly whats going on in this new piece of code and would appreciate a small explanation of the following points:
Apologies in advance if the questions are obvious but any help is appreciated!