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Fig. 12 First stage of the syndesmophyte refinement algorithm: a original
image, b initial
segmentation and c leak correction
where D
2
is a threshold,
ed as partial syndesmophyte with
proportion of bone corresponding to Di.
i
. This second step adds a layer at the
bone/soft tissue boundary where, due to partial volume effect, voxels are likely
to contain both types of tissues.
it
is classi
The thresholds D
1
and D
2
control how selective the algorithm is in admitting
syndesmophyte voxels. They can be used to add partial bone voxels that were not
segmented or exclude soft tissue voxels that were mistakenly labeled as syndes-
mophyte. Both thresholds can be set between 0 and 1. Lower thresholds are more
permissive in syndesmophyte selection. Extensive experimentation led us adopt the
set of threshold (0.8, 0.2) for D
1
and D
2
respectively [
33
].
3 Accuracy and Precision of the Algorithm
3.1 Accuracy and Validity
As an accuracy test, we compared manually and automatically segmented syn-
desmophytes [
33
]. Patients were scanned on either a Philips Brilliance 64 or a GE
Lightspeed Ultra. For both scanners, voltage and current parameters were 120 kVp
and 300 mAs, respectively. Slice thicknesses were 1.5 and 1.25 mm, respectively,
for the Philips and GE. Spacing between slices was 0.7 and 0.625 mm for the
Philips and GE respectively. Each patient was scanned from the middle of the T10
vertebra to the middle of the L4 vertebra providing 4 IDSs for analysis (T11/T12,
T12/L1, L1/L2 and L2/L3). These scanning parameters were used for all the studies
including the reliability and longitudinal studies. Using the ITK-SNAP software
[
54
], one operator manually segmented syndesmophytes in two IDSs (L1/L2 and
L2/L3) for 6 patients. The agreement between manually and automatically seg-
mented syndesmophytes was evaluated using the overlap similarity index (OSI),
also known as the Dice similarity coef
cient [
55
]:
