Skip to Main content Skip to Navigation
Conference papers

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

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.
Complete list of metadatas

Cited literature [19 references]  Display  Hide  Download

https://hal.inria.fr/hal-00813776
Contributor : Project-Team Asclepios <>
Submitted on : Friday, July 5, 2013 - 7:18:37 PM
Last modification on : Tuesday, August 6, 2019 - 11:48:07 AM
Document(s) archivé(s) le : Sunday, October 6, 2013 - 4:11:56 AM

File

menze_10_generative.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Bjoern H. Menze, Koen van Leemput, Danial Lashkari, Marc-André Weber, Nicholas Ayache, et al.. A generative model for brain tumor segmentation in multi-modal images. Medical Image Computing and Computer-Assisted Intervention (MICCAI'10), 2010, Beijing, China. pp.151-159, ⟨10.1007/978-3-642-15745-5_19⟩. ⟨hal-00813776⟩

Share

Metrics

Record views

567

Files downloads

337