BACKGROUND AND PURPOSE: Currently, accurate and reproducible spinal cord GM segmentation remains challenging and a noninvasive broadly accepted reference standard for spinal cord GM measurements is still a matter of ongoing discussion. Our aim was to assess the reproducibility and accuracy of cervical spinal cord GM and WM cross-sectional area measurements using averaged magnetization inversion recovery acquisitions images and a fully-automatic postprocessing segmentation algorithm. MATERIALS AND METHODS: The cervical spinal cord of 24 healthy subjects (14 women; mean age, 40 Ϯ 11 years) was scanned in a test-retest fashion on a 3T MR imaging system. Twelve axial averaged magnetization inversion recovery acquisitions slices were acquired over a 48-mm cord segment. GM and WM were both manually segmented by 2 experienced readers and compared with an automatic variational segmentation algorithm with a shape prior modified for 3D data with a slice similarity prior. Precision and accuracy of the automatic method were evaluated using coefficients of variation and Dice similarity coefficients. RESULTS: The mean GM area was 17.20 Ϯ 2.28 mm 2 and the mean WM area was 72.71 Ϯ 7.55 mm 2 using the automatic method. Reproducibility was high for both methods, while being better for the automatic approach (all mean automatic coefficients of variation, Յ4.77%; all differences, P Ͻ .001). The accuracy of the automatic method compared with the manual reference standard was excellent (mean Dice similarity coefficients: 0.86 Ϯ 0.04 for GM and 0.90 Ϯ 0.03 for WM). The automatic approach demonstrated similar coefficients of variation between intra-and intersession reproducibility as well as among all acquired spinal cord slices. CONCLUSIONS: Our novel approach including the averaged magnetization inversion recovery acquisitions sequence and a fully-automated postprocessing segmentation algorithm demonstrated an accurate and reproducible spinal cord GM and WM segmentation. This pipeline is promising for both the exploration of longitudinal structural GM changes and application in clinical settings in disorders affecting the spinal cord. ABBREVIATIONS: AMIRA ϭ averaged magnetization inversion recovery acquisitions; CV ϭ coefficient of variation; DSC ϭ Dice similarity coefficient; HD ϭ Hausdorff distance; SC ϭ spinal cord T he human spinal cord (SC) can be affected by numerous neurologic disorders of variable pathophysiology (eg, genetic, inflammatory, demyelinating, degenerative, and so forth), 1,2 and MR imaging is a valuable part of the diagnostic work-up in patients with suspected intramedullary pathology. 3,4 SC gray matter and white matter can be involved to a various extent not only among different SC disorders but also among patients with the same disease (eg, multiple sclerosis, amyotrophic lateral sclerosis). 5,6 Hence, quantification of SC compartments may add to our understanding of SC pathology 5,6 and hopefully help in the management of individual patients in the future. However, the SC presents additional challenges for MR...

BACKGROUND AND PURPOSE: Fully automatic quantification methods of spinal cord compartments are needed to study pathologic changes of the spinal cord GM and WM in MS in vivo. We propose a novel method for automatic spinal cord compartment segmentation (SCORE) in patients with MS. MATERIALS AND METHODS:The cervical spinal cords of 24 patients with MS and 24 sex-and age-matched healthy controls were scanned on a 3T MR imaging system, including an averaged magnetization inversion recovery acquisition sequence. Three experienced raters manually segmented the spinal cord GM and WM, anterior and posterior horns, gray commissure, and MS lesions. Subsequently, manual segmentations were used to train neural segmentation networks of spinal cord compartments with multidimensional gated recurrent units in a 3-fold cross-validation fashion. Total intracranial volumes were quantified using FreeSurfer. RESULTS:The intra-and intersession reproducibility of SCORE was high in all spinal cord compartments (eg, mean relative SD of GM and WM: # 3.50% and #1.47%, respectively) and was better than manual segmentations (all P , .001). The accuracy of SCORE compared with manual segmentations was excellent, both in healthy controls and in patients with MS (Dice similarity coefficients of GM and WM: $ 0.84 and $0.92, respectively). Patients with MS had lower total WM areas (P , .05), and total anterior horn areas (P , .01 respectively), as measured with SCORE. CONCLUSIONS:We demonstrate a novel, reliable quantification method for spinal cord tissue segmentation in healthy controls and patients with MS and other neurologic disorders affecting the spinal cord. Patients with MS have reduced areas in specific spinal cord tissue compartments, which may be used as MS biomarkers.

• Lumbar spinal cord segmentation using the semi-automated cord image analyser (Cordial) is feasible. • Lumbar spinal cord is 40-mm cord segment 60 mm above conus medullaris. • Cordial provides excellent inter- and intra-session reproducibility in lumbar spinal cord region. • Cordial shows high potential for application in longitudinal trials.

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