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	Bédard, Sandrine, et al. “Towards contrast-agnostic soft segmentation of the spinal cord.” Medical Image Analysis (2025): 103473.
sc_epi	Banerjee, Rohan, 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, Nilser, 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. (2024). Segmentation model of ex vivo mouse spinal cord white and gray matter (v0.5). Zenodo. https://doi.org/10.5281/zenodo.10819207
lesion_sci_t2	Karthik, Enamundram Naga, et al. “SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury.” International Workshop on Applications of Medical AI. Cham: Springer Nature Switzerland, 2025.
lesion_ms_mp2rage	Valošek, Jan, et al. “Automatic segmentation of the spinal cord nerve rootlets.” Imaging Neuroscience 2 (2024): 1-14.
tumor_edema_cavity_t1_t2	Lemay, Andreanne, 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_deepseg_gm	Perone et al. Spinal cord gray matter segmentation using deep dilated convolutions. Sci Rep 2018
sct_deepseg_lesion	Gros et al. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuroimage 2019
sct_deepseg_sc	Gros et al. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuroimage 2019
sct_get_centerline	Gros et al. Automatic spinal cord localization, robust to MRI contrasts using global curve optimization. Med Image Anal 2018
sct_label_vertebrae	Ullmann et al. Automatic labeling of vertebral levels using a robust template-based approach. Int J Biomed Imaging 2014
sct_process_segmentation `-pmj`	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`	Bédard S, Cohen-Adad J. Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction. Frontiers in Neuroimaging 2022
sct_propseg	De Leener et al. Robust, accurate and fast automatic segmentation of the spinal cord. Neuroimage 2014
sct_propseg `-CSF`	De Leener et al. Automatic segmentation of the spinal cord and spinal canal coupled with vertebral labeling. IEEE Transactions on Medical Imaging 2015
sct_register_multimodal	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 `--param algo=slicereg`	Cohen-Adad et al. Slice-by-slice regularized registration for spinal cord MRI: SliceReg. Proc ISMRM 2015
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_to_template `--param algo=slicereg`	Cohen-Adad et al. Slice-by-slice regularized registration for spinal cord MRI: SliceReg. Proc ISMRM 2015
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 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 et al. White matter atlas of the human spinal cord with estimation of partial volume effect. Neuroimage 2015
Probabilistic atlas (AMU40)	Taso 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	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.