Questions on how to organize training data and generate bounding box ground truth

[zwb0]

Sorry for asking this question,
My training set is CT images consisting of kidney and kidney tumor, whose ground truth is labeled in voxel level. Now I want to do 3D object detection of tumor. I'm wondering how should I organize my training data to feed into the network. And should I generate bounding box ground truth for tumors manually or your library supports to generate bounding box ground truth automatically?
Thanks you in advance!
Best wishes

[CodingHHW]

in the experiments has demo you can learn

[delton137]

I'm trying to do the same thing... will report back if I figure out how..

[bsmarine]

I do not see a demo in experiments showing expected training data hierarchy input, specifically for bboxes and/or voxel-level ground truth (if possible). Anyone have luck figuring this out?

[pfjaegerfb]

you can run, experiments/toy_exp/generate_toys.py . It will create the toy data.

[bsmarine]

Found it, thank you!

[MancaZerovnikMekuc]

I am looking at the 3D LIDC example. Is it possible to specify the format of the data which is returned by preprocessing.py? I would like to avoid the preprocessing of the big LIDC dataset just to see how to prepare my data.

I saw this:
"Images are saved as numpy arrays and the meta-information for the corresponding patient is stored
as a line in the dataframe saved as info_df.pickle."

Just a small example and description of the data preparation for the 3D data would be really helpful. I looked and generated the toy example dataset, but I am interested in 3D data, patch loader and voxel-wise annotation.

@delton137 did you figure it out?