Application of the Dice Similarity Coefficient (DSC) for Failure Detection of a Fully-Automated Atlas Based Knee MRI Segmentation Method

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.