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

Bjoern H. Menze, Koen Van Leemput, Danial Lashkari, Marc-André Weber, Nicholas Ayache, Polina Golland

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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. © 2010 Springer-Verlag.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention
Number of pages7
Volume6362
PublisherSpringer-verlag Berlin
Publication date2010
Pages151-159
ISBN (Print)978-3-642-15744-8
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event13th International Conference on Medical Image Computing and Computer Assisted Intervention - Beijing, China
Duration: 20 Sep 201024 Sep 2010
Conference number: 13
http://www.miccai2010.org/

Conference

Conference13th International Conference on Medical Image Computing and Computer Assisted Intervention
Number13
CountryChina
CityBeijing
Period20/09/201024/09/2010
Internet address
SeriesLecture Notes in Computer Science
ISSN0302-9743

Keywords

  • Hospital data processing
  • Medical computing
  • Medical imaging
  • Tumors
  • Image segmentation

Cite this

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 Medical Image Computing and Computer-Assisted Intervention (Vol. 6362, pp. 151-159). Springer-verlag Berlin. Lecture Notes in Computer Science https://doi.org/10.1007/978-3-642-15745-5_19