Hashing-Based Atlas Ranking and Selection for Multiple-Atlas Segmentation.

In this paper, we present a learning based, registration free, atlas ranking technique for selecting outperforming atlases prior to image registration and multi-atlas segmentation (MAS). To this end, we introduce ensemble hashing, where each data (image volume) is represented with ensemble of hash codes and a learnt distance metric is used to obviate the need for pairwise registration between atlases and target image. We then pose the ranking process as an assignment problem and solve it through two different combinatorial optimization (CO) techniques. We use 43 unregistered cardiac CT Angiography (CTA) scans and perform thorough validations to show the effectiveness and superiority of the presented technique against existing atlas ranking and selection methods.