High resolution volume quantification of the knee joint space based on a semi-automatic segmentation of computed tomography images

Osteoarthritis (OA) is a joint disorder that causes pain, stiffness and decreased mobility. Knee OA presents the greatest morbidity. The main characteristic of OA is the cartilage loss inducing joint space (JS) narrowing. Usually, the progression of OA is monitored by the minimum JS measurement on 2D X-rays images. New dedicated systems based on cone beam computed tomography, providing enough image quality and with favourable dose characteristics are under development. With these new systems, it would be possible to follow the 3D JS changes. High resolution peripheral computed tomography (HR-pQCT) usually used for assessing the trabecular and cortical bone mineral density have been performed on specimen knees with an isotropic voxel of 82 microns. We present here a new semi-automatic segmentation method to measure the 3D local variations of JS. The experiments have been done on HR-pQCT data set and the results have been extended to other computed tomography images with low resolution and/or with cone beam geometry.