How to load my own annotations into Ais

[paloha]

Hello Mart!

I would like to ask whether there is some easy way of importing my own segmentation masks into Ais to create a training dataset. I've noticed that you internally use an .scnt file, which is in essence just a TIFF with some metadata, as mentioned in the docs. However, I am not sure what are the requirements for creating such file from scratch. I have a uint16 mrc file containing many segmentations for multiple classes. Is it enough to convert such segmentations into your file format, or do I need to create some additional labels (such as bounding boxes)? I unfortunately have not found any .scnt writer in the repo that I could reverse engineer.

Thanks & all the best,
Pavol

[mgflast]

Hey Pavol!

The .scnt writer is just python module tifffile and we give the file this .scnt extension - I don't remember why I originally chose to it things that way but here we are. One thing you'll need in that .tiff/.scnt header is a pixel size: tifffile.imwrite(os.path.join(output_directory, out_path), data, description=f"apix={apix}") (if you were planning to train in Ais). Other than that it is just a tif.

To turn your uint16 mrc file into Ais-compatible training data: the dimensions of the image stack are X, Y, N, where XY are 2D image space and N is number of training samples. In the N dimension we have two interleaved stack: (X, Y, 0) would be a (square) ROI cropped from a tomogram, and (X, Y, 1) would be the corresponding label with 0 background, 1 foreground, 2 ignore (but not all models accept ignore labels currently, let me know if that's a problem - it's on the to do list).

The training data may also be 2.5D or 3D. If you wanted to train on tomogram slabs of 128x128x17 (XYZ) voxels for example then (X, Y, 0 - 17) would be the slab, and (X, Y, 18) the label for the central slice. I've not implemented 3D annotation sampling (because 3D annotation is so much effort & when possible always best avoided) so currently the label is only for the central slice.

Hope that helps? Let me know if it's unclear.

[mgflast]

O and make them float32! No need to worry about normalization that happens during the training loop.