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TMI
2010

A Generative Model for Image Segmentation Based on Label Fusion

10 years 6 months ago
A Generative Model for Image Segmentation Based on Label Fusion
We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interp...
Mert R. Sabuncu, B. T. Thomas Yeo, Koenraad Van Le
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TMI
Authors Mert R. Sabuncu, B. T. Thomas Yeo, Koenraad Van Leemput, Bruce Fischl, Polina Golland
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