Groupwise multi-atlas segmentation of the spinal cord's internal structure

Abstract

The spinal cord is an essential and vulnerable component of the central nervous system. Differentiating and localizing the spinal cord internal structure (i.e., gray matter vs. white matter) is critical for assessment of therapeutic impacts and determining prognosis of relevant conditions. Fortunately, new magnetic resonance imaging (MRI) sequences enable clinical study of the in vivo spinal cord's internal structure. Yet, low contrast-to-noise ratio, artifacts, and imaging distortions have limited the applicability of tissue segmentation techniques pioneered elsewhere in the central nervous system. Additionally, due to the inter-subject variability exhibited on cervical MRI, typical deformable volumetric registrations perform poorly, limiting the applicability of a typical multi-atlas segmentation framework. Thus, to date, no automated algorithms have been presented for the spinal cord's internal structure. Herein, we present a novel slice-based groupwise registration framework for robustly segmenting cervical spinal cord MRI. Specifically, we provide a method for (1) pre-aligning the slice-based atlases into a groupwise-consistent space, (2) constructing a model of spinal cord variability, (3) projecting the target slice into the low-dimensional space using a model-specific registration cost function, and (4) estimating robust segmentation susing geodesically appropriate atlas information. Moreover, the proposed framework provides a natural mechanism for performing atlas selection and initializing the free model parameters in an informed manner. In a cross-validation experiment using 67 MR volumes of the cervical spinal cord, we demonstrate sub-millimetric accuracy, significant quantitative and qualitative improvement over comparable multi-atlas frameworks, and provide insight into the sensitivity of the associated model parameters.

Keywords: Cervical spinal cord segmentation; Groupwise registration; Multi-atlas segmentation; Spinal cord internal structure.

Figure 1

Problems associated with non-rigid volumetric registration of cervical spinal cord MRI. Non-rigid volumetric registration of cervical spinal cord MRI is challenging and may yield suboptimal results, including (A) poor global initialization, (B) undesired boundary conditions, and (C) overly smoothed deformations as a result of poor local correspondence.

Figure 2

Flowchart describing the construction of the groupwise consistent atlas representation and the resulting groupwise appearance model. In an iterative procedure, all of the atlases registered to the current estimate of the mean, which is then updated. Using the co-registered atlas data, the groupwise appearance model is constructed using principal component analysis.

Figure 3

Example local atlas image content with respect to the primary modes of variation constructed in the model. The geodesic distance between co-registered atlases (i.e., the distance in the low-dimensional model) visually corresponds to anatomical similarity.

Figure 4

Flowchart describing the process of (1) registering the target image with the model space, and (2) constructing the final segmentation estimate. For a given rigid transform, the registered target is projected into the model space enable a model-informed cost function to be evaluated. This process is repeated until the optimal rigid transformation is found. Using the optimal parameters (i.e., the transform and the selected atlas content), the segmentation estimate is constructed through label fusion and, finally, transferred back to the original target coordinate system.

Figure 5

Quantitative comparison of the considered registration frameworks and label fusion approaches for the accuracy of both gray matter and white matter segmentation. For both structures, the accuracy is measured in terms of the Dice similarity coefficient, mean surface distance error, and Hausdorff distance error. The proposed groupwise slice-based registration framework provides consistent improvement across both structures and by all of the considered metrics.

Figure 6

Slice-based qualitative comparison of a pairwise slice-based registration framework and the proposed groupwise slice-based registration framework. For both examples, the proposed framework provides significantly more accurate segmentations and is able to maintain the complex structure of the GM horn.

Figure 7

Volumetric qualitative comparison of the accuracy of the segmented gray matter for the pairwise slice-based framework, and the proposed groupwise slice-based framework. The proposed registration framework consistently estimates the complex shape of the GM horn more accurately than its pairwise counterpart. Note the different axes for the two different registration frameworks.

Figure 8

Quantitative analysis of the sensitivity of the proposed registration framework to the free model parameters. For the fraction of explained variance, (A), the inclusion of a significant portion of the modes of variation provides valuable benefits in terms of segmentation accuracy (i.e., up to κ = 0.99). However, inclusion of too many modes (i.e., κ = 0.9999) results in sub-optimal performance. For the model weighting parameter, (B), the estimated parameter value inferred from the model appears to be the near-optimal parameter value across the considered targets. For both parameters, the gray bar indicates the value used in the other presented experiments. Note, for (A) and (B) the accuracy measures are the result of a majority vote so that the effect of the parameter is not obfuscated by more sophisticated fusion algorithms.