Segmentation of the human spinal cord

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

Abstract

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.

Keywords

Spinal cord Segmentation White matter Gray matter MRI 

Abbreviations

ALS

Amyotrophic lateral sclerosis

APW

Anteroposterior width

C3

Cervical vertebral level (3rd vertebra)

CAD

Computer-aided diagnosis

COV

Coefficient of variation

CNS

Central nervous system

CSA

Cross-sectional area

CSF

Cerebrospinal fluid

DTbM

Double threshold-based segmentation method

DTI

Diffusion tensor imaging

EDSS

Extended disability status scale

EPI

Echo planar imaging

FAI

Fuzzy anisotropy index

fMRI

Functional MRI

FOV

Field of view

FrAt

Friedreich’s ataxia

FSPGR

Fast spoiled gradient-recalled-echo

GM

Gray matter

GRE

Gradient echo

HC

Healthy control

HDE

Hausdorff distance error

ICC

Intra-class correlation coefficient

LRW

Left–right width

MJD

Machado–Joseph disease

mp-MRI

Multiparametric MRI

MPRAGE

Magnetization prepared rapid acquisition gradient echoes

MP2RAGE

Magnetization prepared 2 rapid acquisition gradient echoes

MRI

Magnetic resonance imaging

MS

Multiple sclerosis

MSDE

Mean surface distance error

MT

Magnetization-transfer imaging

MTR

Magnetization transfer ratio

NMO

Neuromyelitis optica

PCA

Principal component analysis

PSIR

Phase-sensitive inversion recovery

PVE

Partial volume effect

ROI

Region of interest

SCI

Spinal cord injury

SMA

Spinal muscular atrophy

SNR

Signal-to-noise ratio

STAPLE

Simultaneous truth and performance level estimation

STIR

Short inversion time inversion recovery

TBM

Tensor-based morphometry

UHF

Ultra high field

VBM

Voxel-based morphometry

WM

White matter

Ø

Diameter

Notes

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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