Enumerated sparse extraction of important surgical planning features for mandibular reconstruction

Because implicit medical knowledge and experience are used to perform medical treatment, such decisions must be clarified when systematizing surgical procedures. We propose an algorithm that extracts low-dimensional features that are important for determining the number of fibular segments in mandibular reconstruction using the enumeration of Lasso solutions (eLasso). To perform the multi-class classification, we extend the eLasso using an importance evaluation criterion that quantifies the contribution of the extracted features. Experiment results show that the extracted 7-dimensional feature set has the same estimation performance as the set using all 49-dimensional features.