Cui, X.

June, N.

Kim, H.

Kwack, K.-S.

Li, S.

Min, B.-H.

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Poster Presentations / Osteoarthritis and Cartilage 19S1 (2011) S53–S236 S191
411
CT IMAGE SEGMENTATION AND REGISTRATION TO MONITOR
DISEASES AROUND THE KNEE JOINT
N. June1, S. Li1, X. Cui1, K-S. Kwack2, B-H. Min3, H. Kim1. 1Sch. of
Information and Communication Engineering, INHA Univ., INCHEON,
Korea, Republic of ; 2Dept. of Radiology, Ajou Univ. Sch. of Med., SUWON,
Korea, Republic of ; 3Dept. of Orthopaedic Surgery, Ajou Univ. Sch. of Med.,
SUWON, Korea, Republic of
Purpose: Early diagnosis of OA (osteoarthritis) and monitoring treatment
response are very important for OA patients. The follow-up examinations
are frequently used by CT scan or MR imaging. The comparison of each
medical image from different follow-up examinations is very important
for OA treatment. For this comparison, accurate registration of 3D
medical images between different CT examinations of same patient
should be important to overcome unexpected misregistrations originated
from many causes, different patient posture, different ﬁeld of view, and
different resolution of images on each CT scanning. In this study we
attempt to show our new fast and effective registration algorithm for
medical images.
Methods: A dataset of knee CT images are used to validate a hybrid
segmentation and registration method. The CT data is nearly isotropic
and the physical size of voxel is 0.36 x 0.36 x 0.5mm. The fully automatic
segmentation based on Active Contour is employed to extract bone
regions of knee joint image. Then a two-steps 3D rigid body registration
method is performed after segmentation. First, two CT volumes are
initially aligned based on their principal axes. Then, the alignment
is reﬁned by optimizing the similarity score of the image’s voxel. A
normalized cross-correlation (NCC) is used as similarity metric and
downhill simplex method is employed to attain the optimal score. The
registration is separately accomplished for segmented femur and tibia
bone region. For each segmented bone volume, new 20 datasets were
created by applying random rigid transformation along x-, y-, z-axes
varying between ±10 degree for rotation and ±10 pixel for translation.
Table 1. Mean±SD transformation error measurement
Rotation (°) Translation (mm)
a b g tx ty tz
Femur 0.31±0.44 0.27±0.40 0.28±0.36 0.24±0.15 0.32±0.22 0.18±0.19
Tibia 0.18±0.27 0.06±0.05 0.18±0.13 0.22±0.13 0.32±0.19 0.34±0.34
Fig. 1. Femur bone: the superimposed 3D visualization of reference and
ﬂoat volume. (a) before registration, (b) after registration.
Fig. 2. Tibia bone: the superimposed 3D visualization of reference and
ﬂoat volume. (a) before registration, (b) after registration.
Results: The registration accuracy was 0.21 (±0.26 SD) degrees for
rotation and 0.27 (±0.2 SD) mm for translation. The accuracy was
evaluated by mean and standard deviation (SD) of transformation errors
and rotation errors. Table 1 shows the mean (±SD) of transformation
errors and rotation errors of femur and tibia registration. Figure 1 and
Figure 2 show 3D visualization before and after registration of femur and
tibia bone. Red volume denotes the reference volume and green volume
represents ﬂoat volume. The ﬁgures demonstrate that the ﬂoat volumes
were aligned to the same coordinate of reference volumes accurately.
Conclusions: The preliminary study of synthetic datasets showed that
the segmentation and registration method can be readily applicable for
monitoring the knee joint disease.
412
EARLY SUBCHONDRAL BONE MORPHOMETRIC CHANGES IN
OSTEOARTHRITIS: A MICRO-CT STUDY IN THE MURINE JOINT
INSTABILITY MODEL
T. Funck-Brentano1, W. Bouaziz1, E. Hay1, V. Geoffroy1, D. Hannouche2,
M.E. Cohen-Solal1. 1INSERM U606, PARIS, France; 2CNRS UMR 7052, PARIS,
France
Purpose: Subchondral bone is involved in osteoarthritis (OA) both
at early and late stages. Subchondral bone modiﬁcations have been
described in mice models of osteoarthritis at early and late stages.
However, time-dependent effects of changes in subchondral and
trabecular bone are poorly known. The objective of this study is to
analyse the changes of trabecular bone structure along with subchondral
plate in OA using the model of joint instability in mice.
Methods: To analyse the effects of instability, partial meniscectomy
(MNX) of the medial meniscus was performed on right knees of 10 week-
old CD1 mice. A sham operation was performed on controlateral knees.
At 4 and 6 weeks, samples were ﬁxed and prepared for ex vivo micro-
CT acquisition (pixel size 9mm, Skyscan® 1172, Kontich, Belgium). Then,
knees were decalciﬁed and prepared for cryosections. For each medial
compartment, the OARSI OA score (sum of the scores at tibiae and
femurs) was the mean of 3 saggital slide levels stained by Safranin-O.
Morphometric analyzes by micro-CT were performed in the medial tibial
plateau to study the trabecular network. Subchondral plate thickness
was also measured on reconstructed images. Wilcoxon’s tests were
performed.
Results: Animals were sacriﬁced at week 4 and 6. The OA score conﬁrmed
a time-dependent effect of MNX in cartilage degradation (4.00 and 6.22
at 4 and 6 weeks respectively). At week 4, micro-CT showed a marked
decrease in bone volume in MNX knees compared to sham (BV/TV:
47.6% ± 5.0 vs 62.0% ± 4.7, respectively) which was still observed at
week 6 although milder (51.2% ± 2.9 vs 58.0% ± 1.5, p = 0.028). These
results were accompanied by a higher trabecular separation markedly
more severe at week 4 indicating that bone loss was related to a higher
bone resorption. No signiﬁcant effect on the thickness of subchondral
plate was observed at week 4 but a mild increase appeared at week 6
(0.184mm vs 0.171mm, p=0.043).
Conclusions: These preliminary results show that micro-CT evaluation of
tibial epiphysis trabecular network and subchondral bone plate thickness
is a reliable technique in the murine joint instability OA model. This
conﬁrms that an initial decrease in bone volume occurs at the early stages
of the disease. Further experiments with greater number of animals are
required to describe subchondral bone morphometric changes in a time-
dependent fashion.
413
CARTILAGE SIGNAL INTENSITY ON MRI: ASSOCIATION WITH
BODY MASS INDEX, CARTILAGE DEFECTS AND TYPE II COLLAGEN
BREAKDOWN
O. Stannus1, G. Jones1, F. Cicuttini2, C. Ding1,2. 1Univ. of Tasmania, Hobart,
Australia; 2Monash Univ., Melbourne, Australia
Purpose: MRI has enabled the observation of many abnormal features
of OA. However, the ability of MRI to detect early osteoarthritic changes
is limited. Our aim was to develop a semi-automatic computer program
to assess cartilage signal intensity on T1-weighted MRI images, and then
examine whether signal intensity is associated with 1) body mass index
(BMI), 2) cartilage defects, and 3) type II collagen breakdown in young
to middle-aged adults.
Methods: A total of 50 subjects (mean age 41, range 29–57; 64% female)
were randomly selected from the community. MRI scans of right knees
were performed (sagittal T1-weighted fat saturation 3D gradient recall


411 CT IMAGE SEGMENTATION AND REGISTRATION TO MONITOR DISEASES AROUND THE KNEE JOINT 

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411 CT IMAGE SEGMENTATION AND REGISTRATION TO MONITOR DISEASES AROUND THE KNEE JOINT

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