Strategy to re-use existing binary ground truth for enriching contrast-agnostic model

[jcohenadad]

There are a few projects where binary ground truths of good quality already exist. They have been reviewed by a human and are reliable to use for training. However, given that the original mask was created using sct_deepseg_sc, there is an over/under segmentation. Moreover, the mask is binary and we'd rather enrich the contrast-agnostic model using soft mask, in order to avoid reducing the softness of the model prediction (@naga-karthik observed it in previous experiments).

One possible strategy, is to:

• select a dataset (eg: Basel MP2RAGE),
• apply the contrast-agnostic model on the population,
• create a function that computes CSA per slice and per subject
• plots the CSA (one dot per slice and per subject) for both the contrast-agnostic (y-axis) and the native ground truth (x-axis)
• create another function that adds dilation/erosion on the native GT masks, and the kernel should be chosen such that the CSA line is bissectrice of the plot above (ie: perfect level of agreement). Note that the dilation/erosion should be soft, which is possible via scikit-image (see: https://scikit-image.org/docs/stable/api/skimage.morphology.html#skimage.morphology.dilation).
• Once the appropriate kernel is found, apply it to the GT, and call the new GT with suffix, eg: sub-XXX_T1w_label-SC_seg-soft.nii.gz. Or sub-XXX_T1w_label-SC_probseg.nii.gz (although I find the latest one less intuitive, maybe we should revisit our convention @valosekj @sandrinebedard)

[valosekj]

Once the appropriate kernel is found, apply it to the GT, and call the new GT with suffix, eg: sub-XXX_T1w_label-SC_seg-soft.nii.gz. Or sub-XXX_T1w_label-SC_probseg.nii.gz (although I find the latest one less intuitive, maybe we should revisit our convention @valosekj @sandrinebedard)

Agree. sub-XXX_T1w_label-SC_seg-soft.nii.gz is more intuitive than sub-XXX_T1w_label-SC_probseg.nii.gz.
Another possible variants:

• sub-XXX_T1w_label-SC_segsoft.nii.gz (i.e., without the last -)
• sub-XXX_T1w_label-SC_softseg.nii.gz

[jcohenadad]

i like sub-XXX_T1w_label-SC_softseg.nii.gz. I think it makes it more BIDS compliant by not having the hyphen between soft and seg

[valosekj]

i like sub-XXX_T1w_label-SC_softseg.nii.gz.

Me too!

I think it makes it more BIDS compliant by not having the hyphen between soft and seg

Exactly!

Let's wait for @sandrinebedard opinion. I will then update our convention.

[jcohenadad]

also tagging @mguaypaq who is versed into bids

[sandrinebedard]

I agree with sub-XXX_T1w_label-SC_softseg.nii.gz, it also coherent with https://github.com/spine-generic/data-multi-subject/blob/master/derivatives/labels_softseg/sub-amu01/anat/sub-amu01_T1w_softseg.json (which the average softseg created from spine generic)

[mguaypaq]

Some thoughts:

• As a suffix for non-binary segmentations, I think _probseg.nii.gz is what's currently officially in BIDS (reference), but I don't particularly mind _softseg.nii.gz as a suffix. The standard already explicitly allows non-compliant derivatives.

• Either way, the biggest problem for BIDS compliance is actually the _T1w_ part of the filename, because it's not a _key-value_ pair. Normally a filename is only allowed one _suffix part, at the end, which is _softseg or _probseg in this case. The BIDS recommended way to keep the original file _suffix as part of a derivative file name is with a _desc-suffix_ entity (see the next-to-last point here and some of the filename schemas at this link; I have an open pull request to add _desc-suffix_ to the other derivative filename schemas.)

  But the major downside for following this convention is that it's much harder to deal with the conversion between _T1w and _desc-T1w in a simple bash script.

[valosekj]

I guess we concluded to change _probseg.nii.gz to _softseg.nii.gz. I did this in neuropoly/intranet.neuro.polymtl.ca@03ca9af.

Mathieu's point about _desc-suffix_ is relevant and should be kept in mind.

[Nilser3]

First result with fixed kernel:

This is a slice where of the sub-MRS00, that due to the presence of a MS lesion, the contrast-agcostic (C.A.) model has an important subsegmentation, where even if we apply a dilation in this soft masks, we do not get to have a correct soft segmentation of the SC (image 2 - 4).

So, taking the binary GT and doing a dilation with a fixed kernel (see Notebook ), we can obtain a soft mask that keeps the CSA measure (image 5 - 6).

Performing this procedure on all slices, we observe a preserved CSA between GT bin and GT soft

Here is the QC sub-MRS001

I will continue to investigate the entire database to see if there is a significant difference between the CSA from GT bin and soft.
(for this subject the maximum difference was 0.009 mm2 ).

[jcohenadad]

This is excellent @Nilser3 ! I think it's the way to go

[Nilser3]

As previously reported,
Here the QC for 12 subjects from marseille-3T-mp2rage with SC improved and soft masks.

Legend of QC maks

• SC_seg.nii.gz -> sct_deepseg_sc, corrected by rater 1 (Samira)
• SC_seg_rater2.nii.gz -> contrast-agnostic (version v2.0), binarized with threshold 0.5001 corrected by rater 2 (Nilser)
• SC_softseg.nii.gz -> Soft dilation based on rater 2 mask using notebook

Comparison of bin - soft CSA in 12 patients (M0 and M24)

In the QC there is a shrink effect for the soft mask, but it is only visual, here is the real image:

[jcohenadad]

We have two params: the sigma of the gaussian kernel (that creates the softness of the GT), and the dilation/erosion to apply (to match the CSA of the contrast-agnostic model). I suggest we fix the first one (ie: sigma), and then find the appropriate dilation/erosion to match the CSA.

Question: how do we find the appropriate sigma?

[naga-karthik]

Adding my two cents here -- recently, I was trying to play with dilation/erosion and gaussian kernel by applying them to the lesion masks in the context of ms lesion augmentation. I used this approach and saw that softness preservation was okay.

What I learned is that it was easier to fix binary dilation to the default values given in scipy and tweak the sigma value to match the softness we want.