rootlets

Segmentation of spinal nerve rootlets for T2w and MP2RAGE contrasts (T1w-INV1, T1w-INV2, and UNIT1)

This segmentation model for spinal nerve rootlets segmentation uses a 3D U-Net architecture, and was trained with the nnUNetV2 framework. It is a multiclass model, outputting a single segmentation image containing 8 classes representing the C2-T1 dorsal and ventral spinal cord nerve rootlets. Training data included images from healthy subjects across three datasets: spine-generic multi-subject (3T T2w, n=21), OpenNeuro ds004507 (3T T2w, n=7, 10 images), and private data (7T MP2RAGE, n=15, 3 contrasts per subject, 45 images).

Reference

@misc{krejci2025rootletsegdeeplearningmethod,
      title={RootletSeg: Deep learning method for spinal rootlets segmentation across MRI contrasts}, 
      author={Katerina Krejci and Jiri Chmelik and Sandrine Bédard and Falk Eippert and Ulrike Horn and Virginie Callot and Julien Cohen-Adad and Jan Valosek},
      year={2025},
      eprint={2509.16255},
      archivePrefix={arXiv},
      primaryClass={q-bio.TO},
      url={https://arxiv.org/abs/2509.16255}, 
}

Project URL: https://github.com/ivadomed/model-spinal-rootlets

usage: sct_deepseg rootlets [-i <file> [<file> ...]] [-o <str>] [-install]
                            [-custom-url CUSTOM_URL [CUSTOM_URL ...]]
                            [-largest {0,1}] [-fill-holes {0,1}]
                            [-remove-small REMOVE_SMALL [REMOVE_SMALL ...]]
                            [-qc <folder>] [-qc-dataset <str>]
                            [-qc-subject <str>] [-qc-seg <file>] [-h] [-v <int>]
                            [-r {0,1}] [-test-time-aug]

INPUT/OUTPUT

-i

Image filename(s) to segment. If segmenting multiple files, separate filenames with a space.

-o

Output file name. The chosen filename will be used as a base name, and model-specific suffixes will be added to the end depending on the type of output (e.g. ‘_cord.nii.gz’, ‘_gm.nii.gz’, etc.).

TASKS

-install

Install models that are required for specified task.

Default: False

-custom-url

URL(s) pointing to the .zip asset for a model release. This option can be used with -install to install a specific version of a model. To use this option, navigate to the ‘Releases’ page of the model, find release you wish to install, and right-click + copy the URL of the .zip listed under ‘Assets’. Example: sct_deepseg rootlets -install -custom-url CUSTOM_URL sct_deepseg rootlets -i t2.nii.gz

PARAMETERS

-largest

Possible choices: 0, 1

Keep the largest connected object from each output segmentation; if not set, all objects are kept.

Default: 0

-fill-holes

Possible choices: 0, 1

If set, small holes in the segmentation will be filled in automatically.

Default: 0

-remove-small

Minimal object size to keep with unit (mm3 or vox). A single value can be provided or one value per prediction class. Single value example: 1mm3, 5vox. Multiple values example: 10 20 10vox (remove objects smaller than 10 voxels for class 1 and 3, and smaller than 20 voxels for class 2).

-test-time-aug

Perform test-time augmentation (TTA) by flipping the input image along all axes and averaging the resulting predictions. Note: The time it takes to run the model will increase due to the additional predictions.

Default: False

MISC ARGUMENTS

-qc

The path where the quality control generated content will be saved.

-qc-dataset

If provided, this string will be mentioned in the QC report as the dataset the process was run on.

-qc-subject

If provided, this string will be mentioned in the QC report as the subject the process was run on.

-qc-seg

Segmentation file to use for cropping the QC. This option is useful when you want to QC a region that is different from the output segmentation. For example, it might be useful to provide a dilated cord segmentation to expand the QC field of view.

-v

Possible choices: 0, 1, 2

Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode.

Default: 1

-r

Possible choices: 0, 1

Remove temporary files.

Default: 1