Mark images as defaced

[effigies]

Question came up on Neurostars about detecting already defaced images: https://neurostars.org/t/is-it-an-issue-to-run-pydeface-twice/16866

The general problem is difficult, but we could make people's lives easier by marking images as defaced in the descrip header field (at least for NIfTI images):

defaced = nb.load(..., mmap=False)
descrip = f"pydeface {__version__}".encode()
if defaced.header["descrip"]:
    descrip += b"; " + defaced.header["descrip"]
defaced.header[descrip] = descrip[:80]
defaced.to_filename(...)

[poldrack]

+1

On Wed, Sep 23, 2020 at 7:41 AM Chris Markiewicz notifications@github.com wrote:

Question came up on Neurostars about detecting already deface images: https://neurostars.org/t/is-it-an-issue-to-run-pydeface-twice/16866

The general problem is difficult, but we could make people's lives easier by marking images as defaced in the descrip header field (at least for NIfTI images):

defaced = nb.load(..., mmap=False)descrip = f"pydeface {version}".encode()if defaced.header["descrip"]: descrip += b"; " + defaced.header["descrip"]defaced.header[descrip] = descrip[:80]defaced.to_filename(...)

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[vsoch]

For Dicom files there is a known header for this purpose https://dicom.innolitics.com/ciods/cr-image/patient/00120062

[ofgulban]

I like @effigies suggestion. One danger might be writing defaced into header when the defacing was sub-optimal. I think it is hard to avoid object recognition completely to make sure something is defaced at the end.

On a related thought, I came up with a score that relates to the extent of an image being masked. This score yields values close to 0 for "untouched" images, and close to 1 for masked images. It works by taking the ratio of zero voxel counts in the source image and its gradient magnitude derivative. The idea is that if a part of an image is masked (voxel assigned to zeros) the local neighborhood of those voxels will often be exactly zero too. Such a rigid spatial structure does not exist in "untouched" MRI data due to thermal noise. I have tried this on around 10 different MR images of various contrasts and coverages that are defaced or not. I got above 0.9 for defaced images, and below 0.5 for not-defaced images. Putting my script here in case if anyone finds this interesting or useful: https://gist.github.com/ofgulban/5ce784d808187b02c5c7714ef5538579 .

Might be useful to generate a warning in pydeface to say the input might have been masked already etc.

[marcelzwiers]

Why not add a {"Defaced": True} or something like that in the json sidecar file?

[effigies]

I proposed that field in BIDS and got some pushback, so that's stalled: https://github.com/bids-standard/bids-specification/pull/666

Also, I don't think pydeface has any concept of a sidecar file (though it's been some time since I looked into it closely).

[marcelzwiers]

I thought that BIDS allowed the user to add their own private key-value pairs to the sidecar files? Anyhow, that's what I did in the (drmaa capable) BIDS wrapper around pydeface that I made:

https://bidscoin.readthedocs.io/en/latest/finalizing.html#defacing https://github.com/Donders-Institute/bidscoin/blob/master/bidscoin/deface.py#L117

[effigies]

Yes, totally fine to add your own metadata. Just in the absence of a standard, it might not be respected by tools.