Joint Segmentation of Image Ensembles via Latent Atlases

Abstract. Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. However, the availability of comprehensive, reliable and suitable manual segmentations for atlas construction is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas. Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method by segmenting 50 brain MR volumes. Segmentation accuracy for cortical and subcortical structures approaches the quality of state-of-the-art atlas-based segmentation results, suggesting that the latent atlas method is a reasonable alternative when existing atlases are not compatible with the data to be processed.

• Type: Conference
• Year: 2009
• Where: MICCAI
• Authors: Tammy Riklin Raviv, Koen Van Leemput, William M. Wells III, Polina Golland
• Keywords: Latent Atlas Method, Medical Imaging, MICCAI 2009, Single Manual Segmentation, State-of-the-art Atlas-based Segmentation