SCGM example in tutorial

Hi,

I think there might be some flaw in the example uploaded, please correct me if I’m wrong.

According to SCGM challenge, we had to segment the GREY MATTER. Here is a snapshot of what training data and the labels look like (this is in ITKSnap a viewer for medical images, I have loaded the site1-sc01-image.nii.gz from the training set with the corresponding mask site1-sc01-mask-r1.nii.gz)

My understanding is that the grey matter is the red label stuff in the above image, so my trained model should take a slice (or group of slice) as input and output a mask which only highlights red rigion like stuff, however after few epochs (25 epochs) of running the code given in the example we get something like

My doubt being why is the ground truth label a white blob-like stuff, should it not be very narrow and tiny like the red structure in the above ITKSnap image (and hence the prediction should also be something very fine not a blob-like structure)

What is being considered as ground truth in the code?

Please clarify.

Thank you so much for this amazing effort!!

[perone]

Hi @udion, slice_axis=2 means that it will slice the volume on the 2 dimension, in this case on the axial dimension. It doesn’t mean it ignores the other axis, just that it is using one axis to train. There are many approaches to train, including the use of 3D kernels, however the model I made available is a 2d slice-wise model.

[udion]

@perone

I see that all the scans consist of 3 SLICES (am I right?)

and if that is the case then does slice_axis=2 mean that it will only train and test using axis=2?

and if that is the case does that mean that the example given in documentation ignores the other 2 slices and hence we may have to train other models with different slice axis (or a different model which processes 3 slices together)?