Joint Segmentation of Image Ensembles via Latent Atlases
Author(s)Raviv, Tammy Riklin; Van Leemput, Koen; Wells, William M.; Golland, Polina
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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.
12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part I
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009
Springer Berlin / Heidelberg
Riklin Raviv, Tammy et al. “Joint Segmentation of Image Ensembles via Latent Atlases.” Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. Ed. Guang-Zhong Yang et al. LNCS Vol. 5761. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. 272–280.
Author's final manuscript