Identification of Knee Cartilage Changing Pattern

Abstract

This paper studied the changing pattern of knee cartilage using 3D knee magnetic resonance (MR) images over a 12-month period. As a pilot study, we focused on the medial tibia compartment of the knee joint. To quantify the thickness of cartilage in this compartment, we utilized two methods: one was measurement through manual segmentation of cartilage on each slice of the 3D MR sequence; the other was measurement through cartilage damage index (CDI), which quantified the thickness on a few informative locations on cartilage. We employed the artificial neural networks (ANNs) to model the changing pattern of cartilage thickness. The input feature space was composed of the thickness information at a cartilage location as well as its neighborhood from baseline year data. The output categories were ‘changed’ and ‘no-change’, based on the thickness difference at the same location between the baseline year and the 12-month follow-up data. Different ANN models were trained by using CDI features and manual segmentation features. Further, for each type of feature, individual models were trained at different subregions of the medial tibia compartment, i.e., the bottom part, the middle part, the upper part, and the whole. Based on the experiment results, we found that CDI features generated better prediction performance than manual segmentation, on both whole medial tibia compartment and any subregion. For CDI, the best performance in term of AUC was obtained using the central CDI locations (AUC = 0.766), while the best performance for manual segmentation was obtained using all slices of the 3D MR sequence (AUC = 0.656). As experiment results showed, the CDI method demonstrated a stronger pattern of cartilage change than the manual segmentation method, which required up to 6-hour manual delineation of all MRI slices. The result should be further validated by extending the experiment to other compartments.

Multi‐planar 3D knee MRI segmentation via UNet inspired architectures

“…Knee osteoarthritis is a whole joint disease 13 originated by a multifactorial combination of biochemical, 14 biomechanical, 15 systemic, and intrinsic 16 risk factors. The segmentation of cartilages in knee MRI has been employed in a broad range of OA‐related studies, for example, (1) classification and detection of knee OA progression (Ashinsky et al 19 ; Tiulpin et al 20 ; Ashinsky et al 21 ; Chang et al 22 ; Almajalid et al 23 ), imaging biomarker analysis (Hafezi‐Nejad et al 24 ; Schaefer et al 25 ; Shah et al 26 ; Williams et al 27 ), biomechanical modeling (Liukkonen et al 28 ), and stimulation of cartilage degeneration (Peuna et al 29 ; Liukkonen et al 30 ; Mononen et al 31 ). Automated cartilage segmentation has been extensively researched, but remains an active research problem, in part due to the thin to extremely thin (few millimeters to submillimeter) structures of knee cartilage.…”