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

Authors
Publisher
Springer-verlag Berlin
Publication Date
Keywords
  • 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 Deri
  • We Present Experiments On 25 Glioma Patient Data Sets
  • Demonstrating Significant Improvement Over The Traditional Multivariate Tumor Segmentation
  • © 2010 Springer-Verlag
Disciplines
  • Computer Science

Abstract

A generative model for brain tumor segmentation in multi-modal images - DTU Orbit (25/05/14) A generative model for brain tumor segmentation in multi-modal images - DTU Orbit (25/05/14) A generative model for brain tumor segmentation in multi-modal images 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. General information State: Published Authors: Menze, B. H. (Ekstern), Van Leemput, K. (Intern), Lashkari, D. (Ekstern), Weber, M. (Ekstern), Ayache, N. (Ekstern), Golland, P. (Ekstern) Keywords: (Hospital data processing, Medical computing, Medical imaging, Tumors, Image segmentation) Number of pages: 7 Pages: 151-159 Publication date: 2010 Host publication information Title: Medical Image Computing and Computer-Assisted Intervention Volume: 6362 Publisher: Springer-verlag Berlin ISBN (Print): 978-3-642-15744-8 Series: Lecture Notes in Computer Science ISSN (print): 0302-9743 Main Research Area: Technical/natural sciences Conference: 13th International Conference on Medical Image Computing and Computer Assisted Intervention, Beijing, China, 20/09/10 - 20/09/10 DOIs: 10.1007/978-3-642-15745-5_19 Source: dtu Source-ID: n::oai:DTIC-ART:compendex/252251353::33640 Publication: Research - peer-review › Article in proceedings – Annual report year: 2010

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