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

作者：Salem Mostafa^*; Cabezas Mariano; Valverde Sergi; Pareto Deborah; Oliver Arnau; Salvi Joaquim; Rovira Alex; Llado Xavier

来源：NeuroImage-Clinical, 2018, 17: 607-615.

DOI：10.1016/j.nicl.2017.11.015

• Brain
• MRI
• Multiple sclerosis
• Automatic new lesion detection
• Machine learning

Introduction: Longitudinal magnetic resonance imaging (MRI) analysis has an important role in multiple sclerosis diagnosis and follow-up. The presence of new T2-w lesions on brain MRI scans is considered a prognostic and predictive biomarker for the disease. In this study, we propose a supervised approach for detecting new T2-w lesions using features from image intensities, subtraction values, and deformation fields (DF).
Methods: One year apart multi-channel brain MRI scans were obtained for 60 patients, 36 of them with new T2-w lesions. Images from both temporal points were preprocessed and co-registered. Afterwards, they were registered using multi-resolution affine registration, allowing their subtraction. In particular, the DFs between both images were computed with the Demons non-rigid registration algorithm. Afterwards, a logistic regression model was trained with features from image intensities, subtraction values, and DF operators. We evaluated the performance of the model following a leave-one-out cross-validation scheme.
Results: In terms of detection, we obtained a mean Dice similarity coefficient of 0.77 with a true-positive rate of 74.30% and a false-positive detection rate of 11.86%. In terms of segmentation, we obtained a mean Dice similarity coefficient of 0.56. The performance of our model was significantly higher than state-of-the-art methods.
Conclusions: 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.

• 出版日期2018