21 March 2014 Automatic 3D segmentation of spinal cord MRI using propagated deformable models
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Abstract
Spinal cord diseases or injuries can cause dysfunction of the sensory and locomotor systems. Segmentation of the spinal cord provides measures of atrophy and allows group analysis of multi-parametric MRI via inter-subject registration to a template. All these measures were shown to improve diagnostic and surgical intervention. We developed a framework to automatically segment the spinal cord on T2-weighted MR images, based on the propagation of a deformable model. The algorithm is divided into three parts: first, an initialization step detects the spinal cord position and orientation by using the elliptical Hough transform on multiple adjacent axial slices to produce an initial tubular mesh. Second, a low-resolution deformable model is iteratively propagated along the spinal cord. To deal with highly variable contrast levels between the spinal cord and the cerebrospinal fluid, the deformation is coupled with a contrast adaptation at each iteration. Third, a refinement process and a global deformation are applied on the low-resolution mesh to provide an accurate segmentation of the spinal cord. Our method was evaluated against a semi-automatic edge-based snake method implemented in ITK-SNAP (with heavy manual adjustment) by computing the 3D Dice coefficient, mean and maximum distance errors. Accuracy and robustness were assessed from 8 healthy subjects. Each subject had two volumes: one at the cervical and one at the thoracolumbar region. Results show a precision of 0.30 ± 0.05 mm (mean absolute distance error) in the cervical region and 0.27 ± 0.06 mm in the thoracolumbar region. The 3D Dice coefficient was of 0.93 for both regions.
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B. De Leener, B. De Leener, J. Cohen-Adad, J. Cohen-Adad, S. Kadoury, S. Kadoury, } "Automatic 3D segmentation of spinal cord MRI using propagated deformable models", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90343R (21 March 2014); doi: 10.1117/12.2043183; https://doi.org/10.1117/12.2043183
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