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vertebral body deformations; evaluated from the parameters of a 3D super-quadratic
model, which is initialized as an elliptical cylinder and then gradually deformed by
introducing transformations that yield a more detailed representation of the verte-
bral body shape. Their method was validated on 75 CT and 75 MRI vertebrae
extracted from none normal and ten abnormal subjects; showing a success rate of
94.5 and 88.6 %, respectively.
Meanwhile, Kadoury et al. [
46
] proposed a method for inferring articulated spine
models from pre-operative X-ray to intra-operative CT images. This approach
automatically segments the entire spinal column with annotated landmarks by
modeling complex, non-linear patterns of prior deformations from a Riemannian
manifold embedding, showing an accuracy of 0.7
±
1.8 mm for thoracic vertebra
and 2.1
2.5 mm for lumbar vertebra based on the localization of surgical land-
marks. Recently, Rasoulian et al. [
80
] developed a statistical multi-vertebrae shape
and pose model and proposed a registration-based technique to segment the CT
images of spine using a reduced number of registration parameters. Validation on
lumbar vertebrae of 32 subjects shows a mean error less than 2 mm, which the
authors argue, is suf
±
cient for many spinal needle injection procedures, such as
facet joint injections.
In another recent approach that avoids an explicit parametric model of appear-
ance, Glocker et al. [
35
] proposed a vertebrae localization and identi
cation
algorithm which builds upon supervised classification forests. They overcome the
tedious requirement for dense annotations by a semi-automatic labeling strategy.
Extensive evaluation on a dataset of 224 spine CT scans of patients with pathol-
ogies (including high-grade Scoliosis, Kyphosis, and presence of surgical implants)
shows a mean localization error of 12.4 mm and 70 % identi
cation rates on
pathological spines, which outperforms a parametric approach using Regression
Forests and Hidden Markov Models (HMM).
One major effort in vertebrae segmentation was part of a semi-automated ver-
tebra fracture detection system [
2
]. In the segmentation of vertebra, they started
with the CT volume and select the middle slice as a starting point for segmentation.
There are two main steps to train their model: (1) Inter vertebral disc localization
(that leads to vertebra localization as illustrated below). (2) ASMs for each vertebra
level.
For the
first training task, they trained the proposed model in [
5
] by allowing a
radiologist to place a point inside each disc for the six discs enclosing the
ve
lumbar vertebrae. Then they saved this data with the corresponding images to train
the model for the disc localization step (a point inside each disc).
The second training task is the selection of a
fixed set of points (16 points) for
each vertebra. Then produced a separate model for each vertebra level and prepare
the training data required for an ASM (x-, y-coordinates and the image itself).
Figure
14
shows a sample image with the 16 points on the edges of each vertebra as
selected by the expert radiologist.
The steps for the segmentation of the lumbar vertebrae from CT are explained
below in three sub steps: vertebrae localization, vertebra point distribution
