Joint Segmentation of Image Ensembles via Latent Atlases
Name
Golland_Joint segmentation.pdf
Size
356.14 KB
Format
Adobe PDF
Checksum (MD5)
0837f80faf9a799d9e4ffa4ec12413a6
Author(s)
Raviv, Tammy Riklin
Van Leemput, Koen
Wells, William M.
Golland, Polina
Date Issued
October 2009
Journal
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009
Publisher
Springer Berlin / Heidelberg
Citation
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.
Version
Author's final manuscript
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.
Description
12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part I
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-642-04268-3_34
