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Segmentation of the human spinal cord

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Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

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.

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Notes

  1. Label fusion is a method used for resolving pixel conflicts when deforming multiple atlases into a single target.

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

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Authors and Affiliations

  1. Neuroimaging Research Laboratory (NeuroPoly), Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada

    Benjamin De Leener & Julien Cohen-Adad

  2. Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada

    Benjamin De Leener & Julien Cohen-Adad

  3. Aix Marseille Université, IFSTTAR, LBA UMR_T 24, Marseille, France

    Manuel Taso

  4. Aix Marseille Université, CNRS, CRMBM UMR 7339, Marseille, France

    Manuel Taso & Virginie Callot

  5. APHM, Hôpital de la Timone, Pôle d’imagerie médicale, CEMEREM, Marseille, France

    Manuel Taso & Virginie Callot

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  1. Benjamin De Leener
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  2. Manuel Taso
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Correspondence to Virginie Callot.

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Appendices

Appendix 1: Software for sc segmentation

See Table 2.

Table 2 Available software suites
Full size table

Appendix 2: Animal spinal cord segmentation methods

The review focused on segmentation methods for human, however several studies also introduced interesting algorithms for segmenting the spinal cord in animals. Two of them are briefly detailed below.

Automatic segmentation into WM/GM substructures (lateral, dorsal, ventral)

Diffusion tensor imaging (DTI) is now largely used in spinal cord rodent models either to describe the potential alteration/regeneration consequent to the disease or to test the effects of different therapeutic strategies. In these rodent studies, as for human studies, fully automated segmentation into WM/GM subregions (lateral, anterior, posterior) is thus of great importance as it removes a tedious operation during the analysis of the data. In the method developed by Sdika et al. [118], the segmentation process consisted in four steps: (1) a small patch containing the spinal cord was first detected using a machine learning procedure (SVM); (2) the mask of the spinal cord was then computed on a mean diffusivity weighted image (MDWI) using FAST [119]; (3) the WM/GM segmentation (cf. Fig. 18b) was then performed on a diffusion sensitized image along the spinal cord axis, using FAST as well; (4) the spinal cord was finally divided into its left and right side by finding the best symmetry axis on the input WM/GM segmentation image (cf. Fig. 18c), and into ventral and dorsal GM by a line orthogonal to the left/right (L/R) axis (cf. Fig. 18d). To discriminate sub-structures of WM (cf. Fig. 18e), the first point on the L/R axis after the spinal cord mask was determined (P2) and the Ur point (resp Ul), which was the furthest from P2 in the right (resp. left) part of the spinal cord, was used to discriminate right (resp. left) lateral WM from ventral WM. The Dr point (resp Dl), defined as the point of the WM/GM border the furthest from Ur (resp Ul) was used to discriminate right (resp. left) lateral from dorsal WM. Five mice were used as a training group for SVM and the method was tested on the 13 remaining mice. Future works should now include adaptation of the process to pathological mice or to human data.

Fig. 18
figure 18

a DWI, b WM/GM segmentation, c Left/right axis, d final segmentation into dorsal, lateral and ventral WM, as well as ventral and dorsal GM. e Points used to define the substructure of the WM and GM tissues. From [118], with permission from the authors

Full size image

Automatic segmentation using body symmetry

Mukherjee et al. [120] have developed the first method that uses the body symmetry to segment the spinal cord automatically, in order to assist rehabilitation surgery planning. The algorithm is based on an image-gradient-based open-ended active contour and has been applied on T2*-weighted images of cat’s spinal cord. It can be described by the following steps: (1) the axis of symmetry of the body is detected based on the Bhattacharya coefficient, (2) the boundaries of muscle tissues around the spinal cord on one initial slice are located by actively tracing and connecting neighboring pixels of contours and by constraining the detection with the body symmetry, and (3) the contours are deformed on neighboring slices using a dynamic-programming-based edge energy minimization technique [121]. Despite its application and validation on cat’s spinal cord, the authors have designed the algorithm for human spinal cord as well, based on the similarity in size and shape between cats and humans spinal cords.

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De Leener, B., Taso, M., Cohen-Adad, J. et al. Segmentation of the human spinal cord. Magn Reson Mater Phy 29, 125–153 (2016). https://doi.org/10.1007/s10334-015-0507-2

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