Manual Segmentation as Ground-Truth

SCT
1

Hi!

I am looking to evaluate the performance of several segmentation algorithms for spinal MRI (T1, T2, T2*).

Several publications have used manual segmentation as ground-truth to compare to automated segmentations (10.1016/j.neuroimage.2017.03.010, 10.1016/j.neuroimage.2018.09.081).

Whilst I am familiar with SCT (primarily through the excellent tutorials/courses by the SCT group- thank you!), this the first time that I will be analysing my own dataset. I was therefore wondering if I could kindly be guided to any resources regarding manual segmentation of the spinal cord, with the view that I am later able to use my manual segmentations as a gold-standard for comparison to automated segmentation.

Another option would be that perhaps I could upload a couple of manual segmentations and be guided from there?

As a separate question (although related), could someone kindly confirm that in the spinal cord grey matter segmentation challenge (10.1016/j.neuroimage.2017.03.010), all of the algorithms were fully-automated (i.e. manual correction was not allowed)? Apologies if this is a naive question.

Thanks so much for all of the help in advance.

2

Hi,

Thank you for reaching out.

Regarding generating the ground truth, you can generate them manually or run an automatic segmentation and correct them. Here is a video on how to correct for segmentations: https://www.youtube.com/watch?v=lB-F8WOHGeg&t=2s

Manually segmenting the spinal cord is time consuming, and should ideally be done by multiple raters to remove possible inter-rater biases.

We now have a new model to segment the spinal cord in SCT v6.2 ([2310.15402] Towards contrast-agnostic soft segmentation of the spinal cord), in sct_deepseg -task seg_sc_contrast_agnostic that should work on all of your contrasts!

Regarding the gray matter segmentation challenge, in Table 4 you can see if the methods were automatic or not.

Best,

Sandrine

3

Dear Sandrine,

Thank you so much for your kind response.

That is exceptionally useful!

All the best,

Amir