Information Technology Reference
In-Depth Information
was proposed by Michopoulou et al. [
68
] based on three variations of atlas based
segmentation. However, they start from a manually input point for the center of
each disc. Evaluation on 42 normal and 78 degenerated discs showed best per-
formance by the atlas-robust-fuzzy C-Means approach which combines prior ana-
tomical knowledge with fuzzy clustering techniques.
More recently, Neubert et al. [
71
] presented a method for the 3D segmentation of
Vertebral Bodies (VBs) and Intervertebral Discs (IVDs) from the thoracolumbar
region using statistical shape analysis and registration of gray-level intensity pro-
files. Validation on a dataset of high resolution 3D MR SPACE scans from 28
asymptomatic volunteers resulted in Dice values of 0.89 and 0.88 (lumbar and
thoracic IVDs, respectively). Furthermore, Law et al. [
58
] proposed an unsuper-
vised disc segmentation method that employs an Anisotropic Oriented Flux
detection scheme to distinguish the discs from the neighboring structures with
similar intensity, recognize ambiguous disc boundaries, and handle the shape and
intensity variation of the discs. However, they require two user provided points for
initialization. Evaluation on mid-sagittal slices of 69 cases (110 normal vertebrae)
showed an average of 0.92 Dice similarity coef
cient.
Most of the methods presented to date for the segmentation of the spinal cord
from MRI, has been semi-automatic [
21
,
41
,
65
,
74
]. They include various
approaches such as B-spline active surface optimization [
21
], watershed segmen-
tation [
74
] and deformable models [
65
]. Horsfield et al. [
41
] proposed a semi-
automatic method utilizing a constrained active surface model of the cord surface
assess multiple Sclerosis.
In the past few years there has been few research efforts towards the fully
automated spinal cord segmentation. Koh et al. [
53
] developed an approach using
Gradient Vector Flow Field which achieved a similarity index of 0.7 on 52 cases.
They estimated the spinal cord using the magnitude of the gradient vector
ow edge
map, followed by a connected component analysis to remove holes in the seg-
mentation. The same research group [
54
] proposed an unsupervised and fully
automatic method based on an active contour model based on saliency maps,
achieving 0.71 Dice Similarity Index on 60 cases. Similarly, Mukherjee et al. [
69
]
applied an active contour approach, which evolved an image gradient based, open-
ended contour using dynamic programming-based energy-minimization. Evaluation
on MRI scans of cat showed a mean positive correlation of 0.94. More recently,
Chen et al. [
15
] proposed a deformable atlas-based registration combined with a
topology preserving classi
fl
cation to robustly segment the spinal cord and the
CeroSpinal Fluid (CSF).
In a knowledge-based approach to reconstruct the cervical tissues of the cervical
spine, Seifert et al. [
83
] used the Hough transform and knowledge about spine
curvature to
ned by clustering by
considering the center of gravity of the cluster as the disc center. Disc centers are
then used to segment the soft tissues (spinal cord, trachea and discs) from nine
cervical MRIs resulting in 91 % accuracy. However, due to the use of a number of
rules and heuristics, it is not clear if this approach will work for pathological cases.
find initial seed points for discs which are then re
