Citing SCT

If you use SCT in your research or as part of your developments, please always cite the main reference. As well, please cite the reference(s) to the specific tool(s) you utilized, detailed in specific references, whenever possible.

Main Reference

• De Leener B, Levy S, Dupont SM, Fonov VS, Stikov N, Louis Collins D, Callot V, Cohen-Adad J. “SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data.” Neuroimage 2017.

@article{DeLeener201724,
    title = "SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord \{MRI\} data ",
    journal = "NeuroImage ",
    volume = "145, Part A",
    number = "",
    pages = "24 - 43",
    year = "2017",
    note = "",
    issn = "1053-8119",
    doi = "https://doi.org/10.1016/j.neuroimage.2016.10.009",
    url = "https://www.sciencedirect.com/science/article/pii/S1053811916305560",
    author = "Benjamin De Leener and Simon Lévy and Sara M. Dupont and Vladimir S. Fonov and Nikola Stikov and D. Louis Collins and Virginie Callot and Julien Cohen-Adad",
    keywords = "Spinal cord",
    keywords = "MRI",
    keywords = "Software",
    keywords = "Template",
    keywords = "Atlas",
    keywords = "Open-source ",
}

Specific References

DeepSeg

The table below lists the references for sct_deepseg sub-tasks which have one:

DeepSeg Task	References
spinalcord	Karthik EN et al. “Monitoring morphometric drift in lifelong learning segmentation of the spinal cord” arXiv (2025). Bédard S, Karthik EN et al. “Towards contrast-agnostic soft segmentation of the spinal cord.” Medical Image Analysis (2025): 103473.
sc_epi	Banerjee R et al. “EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data.” bioRxiv (2025): 2025-01.
gm_sc_7t_t2star	Medina N et al. “2D multi-class model for gray and white matter segmentation of the cervical spinal cord at 7T.” arXiv preprint arXiv:2110.06516 (2021).
gm_wm_mouse_t1	Cohen-Adad J “Segmentation model of ex vivo mouse spinal cord white and gray matter (v0.5).” Zenodo (2024).
lesion_sci_t2	Karthik EN, Valošek J et al. “SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury.” International Workshop on Applications of Medical AI (2025). Karthik EN, Valošek J et al. “SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans.” Radiol Artif Intell (2024).
lesion_ms_mp2rage	Medina N et al. “Automatic Multiple Sclerosis Lesion Segmentation in the Spinal Cord on 3T and 7T MP2RAGE images. ISMRM 2025 (2025).
rootlets	Krejčí K et al. “RootletSeg: Deep learning method for spinal rootlets segmentation across MRI contrasts.” arXiv preprint arXiv:2509.16255 (2025). Valošek J et al. “Automatic segmentation of the spinal cord nerve rootlets.” Imaging Neuroscience 2 (2024): 1-14.
tumor_edema_cavity_t1_t2	Lemay A et al. “Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning.” NeuroImage: Clinical 31 (2021): 102766.

Command Line Tools

The table below provides individual references for novel methods used in SCT’s Command-Line Tools.

Note

If you are using white matter/grey matter segmentation tools (sct_deepseg_gm/sct_deepseg) and registration tools (sct_register_to_template/sct_register_multimodal) together as part of a pipeline, please also consider this reference:

Dupont SM, De Leener B, Taso M, Le Troter A, Stikov N, Callot V, Cohen-Adad J. “Fully-integrated framework for the segmentation and registration of the spinal cord white and gray matter.” Neuroimage 2017.

Command line script	References
sct_compute_compression	Bédard S, Valošek J et al. “Normalizing spinal cord compression measures in degenerative cervical myelopathy, Spine J. 2025
sct_compute_compression -normalize-hc	Valošek J, Bédard S et al. A database of the healthy human spinal cord morphometry in the PAM50 template space. Imaging Neuroscience 2024; 2 1–15.
sct_detect_compression	Horáková M et al. “Semi-automated detection of cervical spinal cord compression with the Spinal Cord Toolbox QIMS Vol 12, No 4 2022.
sct_deepseg_gm	Perone C et al. “Spinal cord gray matter segmentation using deep dilated convolutions.” Sci Rep 2018.
sct_deepseg_sc / sct_deepseg_lesion	Gros C et al. “Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.” Neuroimage 2019.
sct_get_centerline	Gros C et al. “Automatic spinal cord localization, robust to MRI contrasts using global curve optimization.” Med Image Anal 2018.
sct_image -stitch	Lavdis I, Glocker B et al. Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data Clinical Radiology 2019.
sct_label_vertebrae	Ullmann E et al. “Automatic labeling of vertebral levels using a robust template-based approach.” Int J Biomed Imaging 2014.
sct_process_segmentation -pmj / -normalize	Bédard S, Cohen-Adad J “Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction.” Frontiers in Neuroimaging 2022.
sct_process_segmentation -normalize-PAM50	Valošek J, Bédard S et al. “A database of the healthy human spinal cord morphometry in the PAM50 template space.”. Imaging Neuroscience 2024; 2 1–15.
sct_propseg	De Leener B et al. “Robust, accurate and fast automatic segmentation of the spinal cord.” Neuroimage 2014.
sct_propseg -CSF	De Leener B et al. “Automatic segmentation of the spinal cord and spinal canal coupled with vertebral labeling.” IEEE Transactions on Medical Imaging 2015.
sct_qc	Valošek J, Cohen-Adad J “Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox” Magn. Reson. Med. Sci. 2024.
sct_register_multimodal / sct_register_to_template	De Leener B, Fonov VS, Louis Collins D, Callot V, Stikov N, Cohen-Adad J “PAM50: Unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space.” Neuroimage 2017.
sct_register_multimodal / sct_register_to_template --param algo=slicereg	Cohen-Adad J et al. “Slice-by-slice regularized registration for spinal cord MRI: SliceReg.” Proc ISMRM 2015.
sct_register_multimodal / sct_register_to_template --param algo=dl	Beal E et al. “Contrast-agnostic deep learning–based registration pipeline: Validation in spinal cord multimodal MRI data.” Aperture Neuro 2023.
sct_register_to_template --lrootlet	Bédard S et al. “Rootlets-based registration to the PAM50 spinal cord template” Imaging Neuroscience 2025.
sct_straighten_spinalcord	De Leener B et al. “Topologically-preserving straightening of spinal cord MRI.” J Magn Reson Imaging 2017.

Template and Atlas

The table below provides references relevant to the PAM50 Template used by SCT, including a reference for the template itself, as well as earlier works that the template builds on.

Template/atlas	References
PAM50 template	De Leener B, Fonov VS, Louis Collins D, Callot V, Stikov N, Cohen-Adad J. “PAM50: Unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space.” Neuroimage 2018.
MNI-Poly-AMU template	Fonov VS et al. “Framework for integrated MRI average of the spinal cord white and gray matter: The MNI-Poly-AMU template.” Neuroimage 2014.
White matter atlas	Lévy S et al. “White matter atlas of the human spinal cord with estimation of partial volume effect.” Neuroimage 2015.
Probabilistic atlas (AMU40)	Taso M et al. “A reliable spatially normalized template of the human spinal cord–Applications to automated white matter/gray matter segmentation and tensor-based morphometry (TBM) mapping of gray matter alterations occurring with age.” Neuroimage 2015.
Spinal levels (v6.1 and above)	Frostell A et al. “A Review of the Segmental Diameter of the Healthy Human Spinal Cord” Front. Neurol. 2016
Spinal levels (v6.0 and below)	Cadotte DW, Cadotte A, Cohen-Adad J, Fleet D, Livne M, Wilson JR, Mikulis D, Nugaeva N, Fehlings MG “Characterizing the location of spinal and vertebral levels in the human cervical spinal cord.” AJNR Am J Neuroradiol 2015, 36(4):803-810.