Segmentation of image ensembles via latent atlases
Name
Golland_Segmentation of.pdf
Size
3.23 MB
Format
Adobe PDF
Checksum (MD5)
cafd2f21800559973524297ff32f8b6d
Author(s)
Riklin-Raviv, Tammy
Van Leemput, Koen
Menze, Bjoern H.
Golland, Polina
Wells, William M.
Date Issued
June 2010
Journal
Medical Image Analysis
Publisher
Elsevier
Citation
Riklin-Raviv, Tammy, Koen Van Leemput, Bjoern H. Menze, William M. Wells III, and Polina Golland. “Segmentation of Image Ensembles via Latent Atlases.” Medical Image Analysis 14, no. 5 (October 2010): 654–665.
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 method for joint segmentation of corresponding regions of interest in a collection of aligned images that does not require labeled training data. Instead, a latent atlas, initialized by at most a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The algorithm is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method on two datasets, segmenting subcortical and cortical structures in a multi-subject study and extracting brain tumors in a single-subject multi-modal longitudinal experiment. We compare the segmentation results to manual segmentations, when those exist, and to the results of a state-of-the-art atlas-based segmentation method. The quality of the results supports the latent atlas as a promising alternative when existing atlases are not compatible with the images to be segmented.
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-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.media.2010.05.004
