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23 February 2012MRI based knee cartilage assessment
Osteoarthritis is one of the leading causes of pain and disability worldwide and a major health problem in
developed countries due to the gradually aging population. Though the symptoms are easily recognized and
described by a patient, it is difficult to assess the level of damage or loss of articular cartilage quantitatively. We
present a novel method for fully automated knee cartilage thickness measurement and subsequent assessment
of the knee joint. First, the point correspondence between a pre-segmented training bone model is obtained
with use of Shape Context based non-rigid surface registration. Then, a single Active Shape Model (ASM) is
used to segment both Femur and Tibia bone. The surfaces obtained are processed to extract the Bone-Cartilage
Interface (BCI) points, where the proper segmentation of cartilage begins. For this purpose, the cartilage ASM
is trained with cartilage edge positions expressed in 1D coordinates at the normals in the BCI points. The
whole cartilage model is then constructed from the segmentations obtained in the previous step. An absolute
thickness of the segmented cartilage is measured and compared to the mean of all training datasets, giving as a
result the relative thickness value. The resulting cartilage structure is visualized and related to the segmented
bone. In this way the condition of the cartilage is assessed over the surface. The quality of bone and cartilage
segmentation is validated and the Dice's coefficients 0.92 and 0.86 for Femur and Tibia bones and 0.45 and
0.34 for respective cartilages are obtained. The clinical diagnostic relevance of the obtained thickness mapping
is being evaluated retrospectively. We hope to validate it prospectively for prediction of clinical outcome the
methods require improvements in accuracy and robustness.
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Dirk-Jan Kroon, Przemyslaw Kowalski, Wojciech Tekieli, Els Reeuwijk, Daniel Saris, Cornelis H. Slump, "MRI based knee cartilage assessment," Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83151V (23 February 2012); https://doi.org/10.1117/12.906918