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. © 2010 Springer-Verlag.
CITATION STYLE
Menze, B. H., Van Leemput, K., Lashkari, D., Weber, M. A., Ayache, N., & Golland, P. (2010). A generative model for brain tumor segmentation in multi-modal images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6362 LNCS, pp. 151–159). https://doi.org/10.1007/978-3-642-15745-5_19
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