A generative model for brain tumor segmentation in multi- modal images

Authors:
Bjoern H. Menze;Koen Van Leemput;Danial Lashkari;Marc-André Weber;Nicholas Ayache;Polina Golland
Affiliations:
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology and INRIA Sophia-Antipolis, France;Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology and Radiology, Massachusetts General Hospital, Harvard Medical School and Information and Computer Sc ...;Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology;Diagnostic Radiology, Heidelberg University Hospital, Germany;INRIA Sophia-Antipolis, France;Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Year:
2010

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Abstract

We introduce a generative probabilistic model for segmentation of tumors in multi-dimensional images. The model allows for different tumor boundaries in each channel, reflecting difference in tumor appearance across modalities.We augment a probabilistic atlas of healthy tissue priors with a latent atlas of the lesion and derive the estimation algorithm to extract tumor boundaries and the latent atlas from the image data. We present experiments on 25 glioma patient data sets, demonstrating significant improvement over the traditional multivariate tumor segmentation.