TOPOLOGY PRESERVING AUTOMATIC SEGMENTATION OF THE SPINAL CORD IN MAGNETIC RESONANCE IMAGES

Automatic Segmentation of Spinal Canals in CT Images via Iterative Topology Refinement

Accurate segmentation of the spinal canals in computed tomography (CT) images is an important task in many related studies. In this paper, we propose an automatic segmentation method and apply it to our highly challenging image cohort that is acquired from multiple clinical sites and from the CT channel of the PET-CT scans. To this end, we adapt the interactive random-walk solvers to be a fully automatic cascaded pipeline. The automatic segmentation pipeline is initialized with robust voxelwise classification using Haar-like features and probabilistic boosting tree. Then, the topology of the spinal canal is extracted from the tentative segmentation and further refined for the subsequent random-walk solver. In particular, the refined topology leads to improved seeding voxels or boundary conditions, which allow the subsequent random-walk solver to improve the segmentation result. Therefore, by iteratively refining the spinal canal topology and cascading the random-walk solvers, satisfactory segmentation results can be acquired within only a few iterations, even for cases with scoliosis, bone fractures and lesions. Our experiments validate the capability of the proposed method with promising segmentation performance, even though the resolution and the contrast of our dataset with 110 patient cases (90 for testing and 20 for training) are low and various bone pathologies occur frequently.

Gradient competition anisotropy for centerline extraction and segmentation of spinal cords

Centerline extraction and segmentation of the spinal cord--an intensity varying and elliptical curvilinear structure under strong neighboring disturbance are extremely challenging. This study proposes the gradient competition anisotropy technique to perform spinal cord centerline extraction and segmentation. The contribution of the proposed method is threefold--1) The gradient competition descriptor compares the image gradient obtained at different detection scales to suppress neighboring disturbance. It reliably recognizes the curvilinearity and orientations of elliptical curvilinear objects. 2) The orientation coherence anisotropy analyzes the detection responses offered by the gradient competition descriptor. It enforces structure orientation consistency to sustain strong disturbance introduced by high contrast neighboring objects to perform centerline extraction. 3) The intensity coherence segmentation quantifies the intensity difference between the centerline and the voxels in the vicinity of the centerline. It effectively removes the object intensity variation along the structure to accurately delineate the target structure. They constitute the gradient competition anisotropy method which can robustly and accurately detect the centerline and boundary of the spinal cord. It is validated and compared using 25 clinical datasets. It is demonstrated that the proposed method well suits the applications of spinal cord centerline extraction and segmentation.

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

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.

Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view

Spinal cord segmentation is an important step in the analysis of neurological diseases such as multiple sclerosis. Several studies have shown correlations between disease progression and metrics relating to spinal cord atrophy and shape changes. Current practices primarily involve segmenting the spinal cord manually or semi-automatically, which can be inconsistent and time-consuming for large datasets. An automatic method that segments the spinal cord and cerebrospinal fluid from magnetic resonance images is presented. The method uses a deformable atlas and topology constraints to produce results that are robust to noise and artifacts. The method is designed to be easily extended to new data with different modalities, resolutions, and fields of view. Validation was performed on two distinct datasets. The first consists of magnetization transfer-prepared T2*-weighted gradient-echo MRI centered only on the cervical vertebrae (C1-C5). The second consists of T1-weighted MRI that covers both the cervical and portions of the thoracic vertebrae (C1-T4). Results were found to be highly accurate in comparison to manual segmentations. A pilot study was carried out to demonstrate the potential utility of this new method for research and clinical studies of multiple sclerosis.

Spinal cord quantitative MRI discriminates between disability levels in multiple sclerosis

OBJECTIVE

The clinicoradiologic paradox, or disconnect between clinical and radiologic findings, is frequently encountered in multiple sclerosis (MS), particularly in the spinal cord (SC), where lesions are expected to cause clinical impairment. We aimed to assess whether quantitative diffusion tensor and magnetization transfer imaging measures in the SC can distinguish MS cases of comparable lesion burdens with high and low disability.

METHODS

One hundred twenty-four patients with MS underwent 3-T cervical SC MRI and were categorized into 4 subgroups according to SC lesion count and disability level. Regions of interest circumscribed the SC cross-section axially between C3 and C4. Cross-sectional area, fractional anisotropy (FA), mean diffusivity (MD), perpendicular diffusivity (λ(⊥)), parallel diffusivity (λ(‖)), and magnetization transfer ratio (MTR) were calculated. Differences between patient subgroups were assessed using t tests and linear regression.

RESULTS

FA, MD, λ(⊥), λ(‖), MTR, and SC cross-sectional area were more abnormal in the high- vs low-disability subgroup of patients with low lesion counts (p < 0.05). MRI measures (except λ(‖) and MTR) were more abnormal in the high- vs low-disability subgroup of patients with high lesion counts (p < 0.05). In age- and sex-adjusted comparisons of high- vs low-disability subgroups, all MRI measures retained differences in the low-lesion subgroup, except λ(‖), whereas only FA, MD, and λ(⊥) retained differences in the high-lesion subgroup.

CONCLUSIONS

In this cross-sectional study of patients with MS, quantitative MRI reflects clinically relevant differences beyond what can be detected by conventional MRI. Our findings support the utility of quantitative MRI in clinical settings, where accurate measurement of disease burden is becoming increasingly critical for assessing treatment efficacy.

Robust GM/WM segmentation of the spinal cord with iterative non-local statistical fusion

New magnetic resonance imaging (MRI) sequences are enabling 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. Recently, methods have been presented for cord/non-cord segmentation on MRI and the feasibility of gray matter/white matter tissue segmentation has been evaluated. To date, no automated algorithms have been presented. Herein, we present a non-local multi-atlas framework that robustly identifies the spinal cord and segments its internal structure with submillimetric accuracy. The proposed algorithm couples non-local fusion with a large number of slice-based atlases (as opposed to typical volumetric ones). To improve performance, the fusion process is interwoven with registration so that segmentation information guides registration and vice versa. We demonstrate statistically significant improvement over state-of-the-art benchmarks in a study of 67 patients. The primary contributions of this work are (1) innovation in non-volumetric atlas information, (2) advancement of label fusion theory to include iterative registration/segmentation, and (3) the first fully automated segmentation algorithm for spinal cord internal structure on MRI.

Multiparametric MRI correlates of sensorimotor function in the spinal cord in multiple sclerosis

BACKGROUND

Spinal cord (SC) pathology is a major contributor to clinical disability in multiple sclerosis (MS). Conventional magnetic resonance imaging (MRI), specifically SC-MRI lesion load measures that include lesion count and volume, demonstrate only a modest relationship with the clinical status of MS patients. Although SC cross-sectional area (CSA) correlates better with clinical dysfunction than MRI lesion count, SC atrophy likely signifies irreversible tissue loss. Using quantitative MRI indices sensitive to early and late microstructural changes in the spinal cord, we searched for the presence of better correlations between MRI measures and clinical status in MS.

OBJECTIVES

We investigated whether diffusion-tensor imaging indices and the magnetization-transfer ratio (MTR) were better associated with the clinical status of MS patients than conventional SC-MRI measures.

METHODS

A total of 129 MS patients underwent 3-tesla cervical SC-MRI and quantitative sensorimotor function testing, using the Vibratron-II and dynamometer. Regions-of-interest circumscribed the SC on axial slices between C3-C4. We calculated SC-CSA, fractional anisotropy (FA), mean diffusivity (MD), perpendicular diffusivity (λ perpendicular), parallel diffusivity (λ||) and MTR. We used multivariable linear regression to determine if there were any associations between MRI indices and clinical measures of dysfunction.

RESULTS

All MRI indices were significantly different in subjects with MS versus healthy controls, and between the progressive versus relapsing MS subtypes, with the exception of λ||. In multivariable regression models that were adjusted for age, sex, brain parenchymal fraction, and SC-CSA, the MRI indices independently explained variability in hip flexion strength (p-values: MD, λ perpendicular, λ|| < 0.001; FA = 0.07), vibration sensation threshold (p-values: FA = 0.04; MTR = 0.05; λ perpendicular = 0.06), and Expanded Disability Status Scale scores (p-values: FA = 0.003; MD = 0.03; λ perpendicular = 0.005; MTR = 0.02).

CONCLUSIONS

In a large, heterogeneous MS sample, quantitative SC-MRI indices demonstrated independent associations with system-specific and global clinical dysfunction. Our findings suggest that the indices studied may provide important information about microstructural SC changes and the substrates of limb disability in MS. The identified structure-function relationships underpin the potential utility of these measures in assessments of therapeutic efficacy.