Focused unsupervised image registration for structure-specific population analysis

Population-based analysis of medical images plays an essential role in identification and development of imaging biomarkers. Most commonly the focus lies on a single structure or image region in order to identify variations to discriminate between patient groups. Such approaches require high segmentation accuracy in specific image regions while the accuracy in the remaining image area is of less importance. We propose an efficient ROI-based approach for unsupervised learning of deformable atlas-to-image registration to facilitate structure-specific analysis. Our hierarchical model improves registration accuracy in relevant image regions while reducing computational cost in terms of memory consumption, computation time and consequently energy consumption. The proposed method was evaluated for predicting cognitive impairment from morphological changes of the hippocampal region in brain MRI images showing that next to the efficient processing of 3D data, our method delivers accurate results comparable to state-of-the-art tools.