Treating Osteoarthritis in Knee with Mimicked Interpositional Spacer
In order to detect different clusters of similar shapes at least 500 MRI datasets need to be processed. The datasets are taken from The Osteoarthritis Initiative (OAI) database. The OAI database contains about 5000 patients. Therefore, a selection has been made based on the Kellgren-Lawrence OA-Score which is available for almost all patients. The Kellgren-Lawrence score differentiates between five grades. For each of the five grades 138 female and 145 male patients have been randomly selected resulting in a preselection of 1415 right knee MRI datasets. In a first step 500 of these datasets are processed and analysed. As MRI protocol SAG_3D_DESS_WE (sagittal 3D dual-echo steady state with selective water excitation) is used for bone and cartilage segmentation.
Bone and cartilage of distal femur and proximal tibia are segmented automatically using Statistical Shape Models . Errors in the automatic segmentation are corrected manually. Additionally, for each knee landmarks of the insertion sites of anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) are placed by hand. A schematic overview is given in figure 1.
The following pictures illustrate the single steps of the pipeline.
The aim is to find geometrical clusters in (very) high dimensional data that complicates meaningful clustering. Therefore, a principal component analysis (PCA) is done to reduce the dimensionality. But the PCA is a global technique. Every single point has the same influence on the result. Hence, the idea is to restrict the geometry to a region of interest.
Nevertheless, the results still have too many dimensions for clustering. For that reason, correlation and regression analysis between geometry and clinical parameters are done to achieve further reduction of dimensionality.
 Seim, H., Kainmueller, D., Lamecker, H., Bindernagel, M., Malinowski, J., & Zachow, S. (2010). Model-based auto-segmentation of knee bones and cartilage in MRI data. Proc. MICCAI Workshop Medical Image Analysis for the Clinic - A Grand Challange, 215-223.
|Christoph von Tycowicz, Felix Ambellan, Anirban Mukhopadhyay, Stefan Zachow||A Riemannian Statistical Shape Model using Differential Coordinates||ZIB-Report 16-69||
|Hans Lamecker, Stefan Zachow||Statistical Shape Modeling of Musculoskeletal Structures and Its Applications||Computational Radiology for Orthopaedic Interventions, Springer, pp. 1-23, 2016, isbn: 978-3-319-23481-6||