Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: application in multiple sclerosis

Abstract

A new semi-automatic method for segmenting the spinal cord from MR images is presented. The method is based on an active surface (AS) model of the cord surface, with intrinsic smoothness constraints. The model is initialized by the user marking the approximate cord center-line on a few representative slices, and the compact surface parametrization results in a rapid segmentation, taking on the order of 1 min. Using 3-D acquired T(1)-weighted images of the cervical spine from human controls and patients with multiple sclerosis, the intra- and inter-observer reproducibilities were evaluated, and compared favorably with an existing cord segmentation method. While the AS method overestimated the cord area by approximately 14% compared to manual outlining, correlations between cord cross-sectional area and clinical disability scores confirmed the relevance of the new method in measuring cord atrophy in multiple sclerosis. Segmentation of the cord from 2-D multi-slice T(2)-weighted images is also demonstrated over the cervical and thoracic region. Since the cord center-line is an intrinsic parameter extracted as part of the segmentation process, the image can be resampled such that the center-line forms one coordinate axis of a new image, allowing simple visualization of the cord structure and pathology; this could find wider application in standard radiological practice.

Copyright 2010 Elsevier Inc. All rights reserved.

Figures

Figure 1

Parametrization of the cord surface. The center-line c is parameterised in z and implemented as cubic spline interpolators of the x- and y- coordinates of the center-line curve. The radius r is parameterised in z and θ, the angle between the radius vector and the x-axis. The surface normal vector N is approximated as the vector that is both normal to the center-line tangent vector, and lies in the plane containing both the center-line tangent and the radius vectors.

Figure 2

For the AS method, evolution of the cord active surface at one z location (at the C2/C3 level) from a 3-D T1-weighted dataset. The image is displayed using bi-linear interpolation and thus appears less pixelated than the underlying image data. The cord segmentation algorithm uses a multi-resolution approach with steadily increasing refinement of the surface model by increasing the number of Fourier coefficients and order of polynomial fit to those coefficients that define the radius generator. Not all steps are shown here. A) shows the user-defined initial marker of the cord center-line and the initial surface of constant radius; B) shows the surface at equilibrium with 3 coefficients and a polynomial of order 2 (C3O2); C) shows C5O4; D) shows C7O6; E) shows C11O10; and F) shows C32O11.

Figure 3

Flow diagram for spinal cord segmentation. The algorithm follows a multi-resolution approach, with the level of detail of the cord outline being controlled at each level by the number of Fourier coefficients used in smoothing the radius generator function, and the polynomial order of the variation of these coefficients with z-coordinate. The inner loop shows a two-stage update, first of the radius generator then of the center-line. The convergence criterion for this inner loop is that none of the vertices of the current cord outline changes position during one iteration by more than a small fraction of the nominal cord radius.

Figure 4

Relationship between normalized mean cord cross-sectional area (CSAn) and EDSS score, for a) the Losseff method at C2 and b) the new AS method between C2 and C5. Relapsing-remitting (RR; n=18) and secondary-progressive (SP; n=20) MS patients are shown separately, but for the pooled data r = −0.51 (p < 0.001) for the Losseff method and r = −0.59 (p < 0.001) for the AS method after adjusting for age and sex.

Figure 5

Relationship between normalized mean cord cross-sectional area (CSAn) and ambulation index, for a) the Losseff method at C2 and b) the new AS method between C2 and C5. Relapsing-remitting (RR; n=18) and secondary-progressive (SP; n=20) MS patients are shown separately, but for the pooled data r = −0.58 (p < 0.001) for the Losseff method and r = −0.65 (p < 0.001) for the AS method, after adjusting for age and sex.

Figure 6

Relationship between normalized mean cord cross-sectional area (CSAn) and disease duration, for a) the Losseff method at C2 and b) the new AS method between C2 and C5. Relapsing-remitting (RR; n=18) and secondary-progressive (SP; n=20) MS patients are shown separately, but for the pooled data, statistically-significant relationships are seen for neither the Losseff method (r = −0.17; p =0.32) nor the AS method (r = −0.30; p = 0.074), after adjusting for age and sex.

Figure 7

Segmentation and unwrapping of the cord in an axial 2-D T2-weighted image. E and F show the original images reconstructed to sagittal and coronal views, while A (C2/C3 level), B (C6/C7), C (T4/T5) and D (T9/T10) show the cord segmentation at various representative levels down the cord as indicated. G and H show the unwrapped cord image in sagittal and coronal views over approximately the same section of cord, while the graph on the right indicates the cord cross-sectional area, measured in a plane perpendicular to the cord center-line. Note the high intensity artifacts seen at the T4/T5 and T9/T10 levels (arrows), which are caused by fact that the table moved between slice blocks. See Methods for more details of the scan acquisition parameters.