Direct estimation of patient attributes from anatomical MRI based on multi-atlas voting

MRI brain atlases are widely used for automated image segmentation, and in particular, recent developments in multi-atlas techniques have shown highly accurate segmentation results. In this study, we extended the role of the atlas library from mere anatomical reference to a comprehensive knowledge database with various patient attributes, such as demographic, functional, and diagnostic information. In addition to using the selected (heavily-weighted) atlases to achieve high segmentation accuracy, we tested whether the non-anatomical attributes of the selected atlases could be used to estimate patient attributes. This can be considered a context-based image retrieval (CBIR) approach, embedded in the multi-atlas framework. We first developed an image similarity measurement to weigh the atlases on a structure-by-structure basis, and then, the attributes of the multiple atlases were weighted to estimate the patient attributes. We tested this concept first by estimating age in a normal population; we then performed functional and diagnostic estimations in Alzheimer's disease patients. The accuracy of the estimated patient attributes was measured against the actual clinical data, and the performance was compared to conventional volumetric analysis. The proposed CBIR framework by multi-atlas voting would be the first step toward a knowledge-based support system for quantitative radiological image reading and diagnosis.