Name
Abstract | ||
|---|---|---|
Spinal cord segmentation provides measures of atrophy and facilitates group analysis via inter-subject correspondence. Automatizing this procedure enables studies with large throughput and minimizes user bias. Although several automatic segmentation methods exist, they are often restricted in terms of image contrast and field-of-view. This paper presents a new automatic segmentation method (PropSeg) optimized for robustness, accuracy and speed. The algorithm is based on the propagation of a deformable model and is divided into three parts: firstly, an initialization step detects the spinal cord position and orientation using a circular Hough transform on multiple axial slices rostral and caudal to the starting plane and builds an initial elliptical tubular mesh. Secondly, a low-resolution deformable model is 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 local contrast-to-noise adaptation at each iteration. Thirdly, a refinement process and a global deformation are applied on the propagated mesh to provide an accurate segmentation of the spinal cord. Validation was performed in 15 healthy subjects and two patients with spinal cord injury, using T1- and T2-weighted images of the entire spinal cord and on multiecho T2*-weighted images. Our method was compared against manual segmentation and against an active surface method. Results show high precision for all the MR sequences. Dice coefficients were 0.9 for the T1- and T2-weighted cohorts and 0.86 for the T2*-weighted images. The proposed method runs in less than 1min on a normal computer and can be used to quantify morphological features such as cross-sectional area along the whole spinal cord. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neuroimage.2014.04.051 | NeuroImage |
Keywords | Field | DocType |
Spinal cord segmentation,Deformable model,Propagation,MRI,Automatic | Spinal cord,Computer vision,Spinal cord injury,Anatomy,Segmentation,Hough transform,Psychology,Cognitive psychology,Robustness (computer science),Artificial intelligence,Initialization,Entire spinal cord | Journal |
Volume | ISSN | Citations |
98 | 1053-8119 | 22 |
PageRank | References | Authors |
1.01 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| De Leener, B. | 1 | 104 | 7.69 |
| Samuel Kadoury | 2 | 252 | 37.48 |
| Julien Cohen-Adad | 3 | 472 | 29.21 |