ivadomed / ivadomed/model-spinal-rootlets
Rootlets-informed lumbar registration
- Dominant language
- Python
- Stars
- 8
- Forks
- 2
- PR merge metrics
- No merged PRs in 30d
Description
## Description
Similarly to the cervical spinal cord ([repo](https://github.com/sct-pipeline/rootlets-informed-reg2template), [preprint](https://arxiv.org/abs/2505.00115)), we could leverage rootlets or spinal levels to inform the registration to the PAM50 template.
> [!NOTE]
> Since lumbar rootlet segmentation has proven to be challenging, we may need to manually label the spinal levels based on the rootlet entry zones. Or develop a method to do so automatically. (For the subject below, manually segmented rootlets are available.)

Pseudo-commands:
```bash
# 1. Cropping
# I cropped the image manually to reduce its size in FSLeyes using Tools/Crop. I saved it with the suffix _roi
# 2. Cord segmentation
sct_deepseg spinalcord -i sub-CTS04_ses-SPpre_acq-ax_T2w_roi.nii.gz
# 3. Cord segmentation labeling
# I manually labelled the cord segmentation in FSLeyes based on the rootlets entry zones. I saved it with the suffix roi_seg_labeled
# 4. Rootlet midpoints
sct_label_utils -i sub-CTS04_ses-SPpre_acq-ax_T2w_roi_seg_labeled.nii.gz -cubic-to-point -o labels.nii.gz
# 5. Registration
sct_register_to_template -i sub-CTS04_ses-SPpre_acq-ax_T2w_roi.nii.gz -s sub-CTS04_ses-SPpre_acq-ax_T2w_roi_seg.nii.gz -lspinal labels.nii.gz -c t2
```
## TODO
- [ ] tweak registration parameters
- [ ] try different number of labels (2 or 3+; [details](https://spinalcordtoolbox.com/stable/user_section/tutorials/vertebral-labeling/how-many-labels-for-registration.html))
Contributor guide
No contributing guide indexed for this repository
Research direction
Start by reproducing the listed cropping, spinal-cord segmentation, labeling, midpoint extraction, and sct_register_to_template commands on the provided subject. Compare registration results while varying the spinal-level label count and registration parameters; done means documenting a reliable configuration or identifying the remaining limitations.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 32/100