## Dealing with empty frames in MRI images

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I started working on the application of deep learning in medical imaging recently. While dealing with MRI images in the BraTS dataset, I observe that first and last few frames are always completely empty (black). I want to ask those who are already working in the field, is there a way to remove them in a procedural manner before training and add them correctly after the training as a postprocessing step (to comply with the ground truth segmentations' shape)? Has anyone tried that? I could not find any results on Google. So asking here.

Edit: I think I did not make my point clear enough. I meant to say first and last few frames of each MRI scan are empty. How to deal with those is what I intended to ask.

Generally when dealing with MRIs, I do this with a script (think of is as a preprocessing step) I run on each image that counts the amount of pixels that have a positive intensity (I actually add a small value to this to account for noise). Let's say for images with values 0-255 I count the amount of pixels with an intensity of over 10-15, let's call this $$x$$. After that I set a threshold (empirically), let's call it $$t$$ and discard images with $$x < t$$.