Karl G. Baum1, Edward Schreyer1, Saara Totterman1, Joshua Farber1, Jose Tamez-Pea1, Patricia Gonzlez1
Quantitative analysis of MRI images is providing new insight into and sensitivity to detect osteoarthritic progression, but is encumbered with the time, cost and variability associated with manual or semi-automated segmentation. To address this, a fully-automated knee MRI segmentation and analysis method was developed and validated. Although the method has proven to be robust, in a small percentage of cases (< 2%) underlying image quality or other anomalies may produce poor segmentation results. This study examines the feasibility of using the Dice Similarity Coefficient (DSC) as an objective, reproducible and automated method of accurately detecting segmentation failure.