Neonatal Brain Structural Connectome

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5

Ok I will try to follow your pipeline as well as your atlas. Thank you very much for your precious help.
Rosella

6

Hi,
I am going to start using your alternative algorithm to 5ttgen for tissue segmentation of infant data. However, I have 3dT1 as structural data. Does it work all the same instead of using T2?
Of note, you find attached:

  1. the 5tt.mif image I have obtained trying to apply default 5ttgen algorithm to one of my 3dT1.
    image
    image628×630 81 KB
  2. the WM fODFs resulting from multi shell multi tissue csd displayed together with the tissue signals contribution map
    image
    Do you think these results are not good enough ?
    thanks
    Rosella
7

Hi,

The dHCP pipeline won’t work with the T1. You can design your own framework using the T1 templates from the mcrib atlas.

I don’t think the segmentation is good, I can not see the separation between the CSF and the WM. In the FODs the same, WM and CSF are mixed and the GM is almost gone.

Best regards,

Manuel

1 Like
8

thank you very much. I will look for the corresponding T2 images , or implement my own framework
Thanks
Rosella

9

Hi,
would you suggest some starting point for designing my own framework for segmenting T1 images?
thanks
Rosella

10

Hi,

You can do it with the manually segmented T1 images from the mcrib atlas and the script antsJointlabelfusion.sh (or something like this, I don’t remember exactly the name). Then with any software you can combine the labels you are interested in.

Finally, for the parcellation, you can use in the same output labelconvert to get the desired labels. I hope this helps.

Best regards,

Manuel

1 Like
11

Ok, thank you. However, I do not understand how to get * seg_mrtrix.nii.gz files (you derived from dHCP pipeline) in order to run antsJointLabelFusion script

antsJointLabelFusion.sh -d 3 -t ${BASENAME}_T2w_restore_brain.nii.gz -x ${BASENAME}_brainmask_drawem.nii.gz -o ${BASENAME}_MCRIB_Structural_ -q 0 -g ${BASENAME}_P01_hist.nii.gz -l P01_seg_mrtrix.nii.gz -g ${BASENAME}_P02_hist.nii.gz -l P02_seg_mrtrix.nii.gz -g ${BASENAME}_P03_hist.nii.gz -l P03_seg_mrtrix.nii.gz -g ${BASENAME}_P04_hist.nii.gz -l P04_seg_mrtrix.nii.gz -g ${BASENAME}_P05_hist.nii.gz -l P05_seg_mrtrix.nii.gz -g ${BASENAME}_P06_hist.nii.gz -l P06_seg_mrtrix.nii.gz -g ${BASENAME}_P07_hist.nii.gz -l P07_seg_mrtrix.nii.gz -g ${BASENAME}_P08_hist.nii.gz -l P08_seg_mrtrix.nii.gz -g ${BASENAME}_P09_hist.nii.gz -l P09_seg_mrtrix.nii.gz -g ${BASENAME}_P10_hist.nii.gz -l P10_seg_mrtrix.nii.gz

thank you

12

Hi,

That’s because I used labelconvert before that command. You need to replace the T2w for the T1w and use the T1w atlases with the segmented images.

You could be interested also to save the tissue probability maps, check for the correct flag to do that.

Best regards,

Manuel

13

Hi,
thank you. My precise question is how to obtain ID_seg.nii.gz images if I can’t use dhHCP pipeline.
labelconvert **ID_seg.nii.gz** M-CRIB_labels_FreeSurfer_format.txt M-CRIB_labels_FreeSurfer_format_default.txt ID_seg_mrtrix.nii.gz
thank you
Rosella

14

Hi,

That files are provided with the mcrib atlas, are the manually segmented images.

Best regards,

Manuel

1 Like
15

Ok. So you mean the hard_segmentations files within MCRIB atlas?
And the first step would be use labelconvert like this?
labelconvert ID_seg.nii.gz M-CRIB_labels_FreeSurfer_format.txt M-CRIB_labels_FreeSurfer_format_default.txt ID_seg_mrtrix.nii.gz
where ID_seg.nii.gz is replaced by M-CRIB_hardseg_*.nii.gz ?
thank you as always
Rosella

16

Hi,

Yes, you can find it here OSF | Original M-CRIB neonatal brain atlas manual parcellations

Best regards,

Manuel

1 Like
17

Hi,

I would run the label convert at the end. The antsJointfusion should be executed first, adding as an output the probability labels. Then you combine the probability labels you need to obtain the 5TT file and after that in the hard labels you run labelconvert.

Best regards,

Manuel

18

ok thank you . In the command below,

  • ${BASENAME}_T1w_restore_brain.nii.gz is my subject’s skull stripped T1

  • ${BASENAME}_brainmask_drawem.nii.gz is its corresponding mask

  • ${BASENAME}_P01_hist.nii.gz is obtained from mrhistmatch command

  • P01_seg_mrtrix.nii.gz is the manually segmented labels from MCRIB atlas for each of the 10 subjects
    antsJointLabelFusion.sh -d 3 -t ${BASENAME}_T1w_restore_brain.nii.gz -x ${BASENAME}_brainmask_drawem.nii.gz -o ${BASENAME}_MCRIB_Structural_ -q 0 -g ${BASENAME}_P01_hist.nii.gz -l P01_seg_mrtrix.nii.gz -g ${BASENAME}_P02_hist.nii.gz -l P02_seg_mrtrix.nii.gz -g ${BASENAME}_P03_hist.nii.gz -l P03_seg_mrtrix.nii.gz -g ${BASENAME}_P04_hist.nii.gz -l P04_seg_mrtrix.nii.gz -g ${BASENAME}_P05_hist.nii.gz -l P05_seg_mrtrix.nii.gz -g ${BASENAME}_P06_hist.nii.gz -l P06_seg_mrtrix.nii.gz -g ${BASENAME}_P07_hist.nii.gz -l P07_seg_mrtrix.nii.gz -g ${BASENAME}_P08_hist.nii.gz -l P08_seg_mrtrix.nii.gz -g ${BASENAME}_P09_hist.nii.gz -l P09_seg_mrtrix.nii.gz -g ${BASENAME}_P10_hist.nii.gz -l P10_seg_mrtrix.nii.gz

Is this first command applied correctly?

19

I never use 5ttgen for this cohort (I never made it work properly), instead I use specific neonatal segmentation algorithm, the dHCP pipeline for example. You can see the results I obtain here.

I’m sure you’d make a number of people happy by taking those instructions, implementing them in a Python script, and contributing it as a novel 5ttgen algorithm :stuck_out_tongue:

Finally, I didn’t understand completely how to use labelconvert command:

See documentation page here. It’s a matter of manually defining a file that contains only those nodes of the parcellation that you wish to retain.

1 Like
20

Hi @rsmith,

I’ll be happy to try it, is there any place I can read how to create a 5tt algorithm?

Best regards,

Manuel

21

is there any place I can read how to create a 5tt algorithm?

No; I’ve intended to do this for some time. I did recently have @rmito document the process of contributing a new Python script to MRtrix3 for the sake of a presentation, and I’d like to generate some more public documentation based on that experience. The software engineering side of things (i.e. not the novel content but the mechanisms of contributing such) is described here.

Contributing to scripts that use the algorithm module (currently 5ttgen, dwi2response, dwibiascorrect, dwinormalise) is slightly different to adding a new standalone script.
If you:

  1. Generate a copy of an existing functioning script in e.g. lib/mrtrix3/_5ttgen/ and give it an appropriate name;
  2. Edit this line so that the name of the parser matches the name of the new file from step 1

, then 5ttgen should present this “new” entry as an algorithm that can be selected and executed from the command-line. From there it’s a matter of modifying the code relating to the command-line interface and the internal execution of commands in order to make the script manifest the behaviour you want. But starting from something that already works, and making small changes and seeing their influence, is always a good entry point.

Rob

22

Thanks Robert! I’ll start with this.

I’ll get back to you once I have something functional.

Best regards,

Manuel

23

Hi @rsmith,

I have a version of the algorithm that works. We are currently testing it in different populations and with different options. I’ll send you the code and the results as soon as we have the results so you can have a look.

Best regards,

Manuel

1 Like
24

Hi @rsmith,

I sent you an email with the code and some results. Let me know what do you think.

Best regards,

Manuel