Analysis on Semi-Automated Knee Cartilage Segmentation Model using Inter-Observer Reproducibility

Osteoarthritis is the most common joint disease without any effective cure to halt its' prevalence. Various medical image segmentation techniques have been proposed to extract knee cartilage from magnetic resonance image in order to identify suitable biomarkers to study the progression of osteoarthritis. In this paper, we propose the use of a random walks knee cartilage segmentation model and analyze the model's accuracy using inter-observer reproducibility. For instance, the proposed model has exhibited promising reproducibility compared to manual segmentation in both normal and diseased categories. In normal cartilage segmentation, the proposed model has shown reproducibility index of 0.93±0.022 while in diseased cartilage segmentation, the proposed model has shown reproducibility index of 0.90±0.049. The results suggest that random walks semi-automated segmentation model reduces the level of ambiguity experienced by manual segmentation model; thus establishing the technique as suitable computerized segmentation technique for knee cartilage segmentation.