Segmentation of the human spinal cord

  • Benjamin De Leener
  • Manuel Taso
  • Julien Cohen-Adad
  • Virginie Callot
Review Article


Segmenting the spinal cord contour is a necessary step for quantifying spinal cord atrophy in various diseases. Delineating gray matter (GM) and white matter (WM) is also useful for quantifying GM atrophy or for extracting multiparametric MRI metrics into specific WM tracts. Spinal cord segmentation in clinical research is not as developed as brain segmentation, however with the substantial improvement of MR sequences adapted to spinal cord MR investigations, the field of spinal cord MR segmentation has advanced greatly within the last decade. Segmentation techniques with variable accuracy and degree of complexity have been developed and reported in the literature. In this paper, we review some of the existing methods for cord and WM/GM segmentation, including intensity-based, surface-based, and image-based methods. We also provide recommendations for validating spinal cord segmentation techniques, as it is important to understand the intrinsic characteristics of the methods and to evaluate their performance and limitations. Lastly, we illustrate some applications in the healthy and pathological spinal cord. One conclusion of this review is that robust and automatic segmentation is clinically relevant, as it would allow for longitudinal and group studies free from user bias as well as reproducible multicentric studies in large populations, thereby helping to further our understanding of the spinal cord pathophysiology and to develop new criteria for early detection of subclinical evolution for prognosis prediction and for patient management. Another conclusion is that at the present time, no single method adequately segments the cord and its substructure in all the cases encountered (abnormal intensities, loss of contrast, deformation of the cord, etc.). A combination of different approaches is thus advised for future developments, along with the introduction of probabilistic shape models. Maturation of standardized frameworks, multiplatform availability, inclusion in large suite and data sharing would also ultimately benefit to the community.


Spinal cord Segmentation White matter Gray matter MRI 



Amyotrophic lateral sclerosis


Anteroposterior width


Cervical vertebral level (3rd vertebra)


Computer-aided diagnosis


Coefficient of variation


Central nervous system


Cross-sectional area


Cerebrospinal fluid


Double threshold-based segmentation method


Diffusion tensor imaging


Extended disability status scale


Echo planar imaging


Fuzzy anisotropy index


Functional MRI


Field of view


Friedreich’s ataxia


Fast spoiled gradient-recalled-echo


Gray matter


Gradient echo


Healthy control


Hausdorff distance error


Intra-class correlation coefficient


Left–right width


Machado–Joseph disease


Multiparametric MRI


Magnetization prepared rapid acquisition gradient echoes


Magnetization prepared 2 rapid acquisition gradient echoes


Magnetic resonance imaging


Multiple sclerosis


Mean surface distance error


Magnetization-transfer imaging


Magnetization transfer ratio


Neuromyelitis optica


Principal component analysis


Phase-sensitive inversion recovery


Partial volume effect


Region of interest


Spinal cord injury


Spinal muscular atrophy


Signal-to-noise ratio


Simultaneous truth and performance level estimation


Short inversion time inversion recovery


Tensor-based morphometry


Ultra high field


Voxel-based morphometry


White matter




Compliance with ethical standards

Conflict of interest

The authors declare they have no conflicts of interest.


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Copyright information

© ESMRMB 2015

Authors and Affiliations

  • Benjamin De Leener
    • 1
    • 2
  • Manuel Taso
    • 3
    • 4
    • 5
  • Julien Cohen-Adad
    • 1
    • 2
  • Virginie Callot
    • 4
    • 5
  1. 1.Neuroimaging Research Laboratory (NeuroPoly), Institute of Biomedical EngineeringPolytechnique MontrealMontrealCanada
  2. 2.Functional Neuroimaging Unit, CRIUGMUniversité de MontréalMontrealCanada
  3. 3.Aix Marseille Université, IFSTTAR, LBA UMR_T 24MarseilleFrance
  4. 4.Aix Marseille Université, CNRS, CRMBM UMR 7339MarseilleFrance
  5. 5.APHM, Hôpital de la Timone, Pôle d’imagerie médicale, CEMEREMMarseilleFrance

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