A New Approach to Fully Automated Fiber Tract Clustering Using Affinity Propagation

Alexander Leemans¹, Derek K. Jones¹

In this work, we present a novel approach to fiber tract clustering approach on the recently introduced concept of affinity propagation (AP). In contrast to other clustering methods, AP clustering allows one to (i) produce tract exemplars; (ii) incorporate asymmetric tract distance measures (e.g., Hausdorff metric); and importantly (iii) determine the number of clusters automatically. Here, we demonstrate 1) the superior performance of AP over spectral and hierarchical clustering methods and 2) how the AP method improves atlas-based tract segmentations.