A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis

The performance of the proposed method shows the benefits of using DF operators as features to train a supervised learning model. Compared to other methods, the proposed model decreases the number of false-positives while increasing the number of true-positives, which is relevant for clinical settings.