While the automatic detection of AD has been widely studied, the problem of the prediction of AD (or its early detection) has been less investigated. This might be explained by the fact that the prediction problem is clearly more challenging since the anatomical changes are more subtle. However, from a clinical point of view the prediction of AD is the key question since it is in that moment when treatment is possible. The potential use of structural MRI as imaging biomarker for Alzheimer’s disease (AD) for early detection has become generally accepted, especially the use of atrophy of entorhinal cortex (EC) and hippocampus (HC). Therefore, in this study, we analyze the capabilities of the recently proposed method, SNIPE (Scoring by Nonlocal Image Patch Estimator), for the early detection of AD to analyze EC and HC atrophy over the entire ADNI database (834 subjects). During validation, the detection (AD vs. CN) and the prediction (pMCI vs. sMCI) efficiency of SNIPE were studied. The obtained results showed that SNIPE obtained competitive or better results than HC volume, cortical thickness and TBM. Moreover, results indicated that MRI grading-based biomarkers are more relevant than volume-based biomarkers. Finally, the success rate obtained by SNIPE was 90% for detection (AD vs. CN) and 74% for prediction (pMCI vs. sMCI).
Main Research Area:
Medical Image Computing and Computer Assisted Intervention, 2012