Brain lesion segmentation through physical model estimation

Marcel Prastawa, Guido Gerig

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Segmentations of brain lesions from Magnetic Resonance (MR) images is crucial for quantitative analysis of lesion populations in neuroimaging of neurological disorders. We propose a new method for segmenting lesions in brain MRI by inferring the underlying physical models for pathology. We use the reaction-diffusion model as our physical model, where the diffusion process is guided by real diffusion tensor fields that are obtained from Diffusion Tensor Imaging (DTI). The method perorms segmentation by solving the inverse problem, where it determines the optimal parameters for the physical model that generates the observed image. We show that the proposed method can infer reasonable models for multiple sclerosis (MS) lesions and healthy MRI data. The method has potential for further extensions with different physical models or even non-physical models based on existing segmentation schemes.

    Original languageEnglish (US)
    Title of host publicationAdvances in Visual Computing - 4th International Symposium, ISVC 2008, Proceedings
    Pages562-571
    Number of pages10
    EditionPART 1
    DOIs
    StatePublished - Dec 1 2008
    Event4th International Symposium on Visual Computing, ISVC 2008 - Las Vegas, NV, United States
    Duration: Dec 1 2008Dec 3 2008

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 1
    Volume5358 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other4th International Symposium on Visual Computing, ISVC 2008
    CountryUnited States
    CityLas Vegas, NV
    Period12/1/0812/3/08

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    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

    Cite this

    Prastawa, M., & Gerig, G. (2008). Brain lesion segmentation through physical model estimation. In Advances in Visual Computing - 4th International Symposium, ISVC 2008, Proceedings (PART 1 ed., pp. 562-571). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5358 LNCS, No. PART 1). https://doi.org/10.1007/978-3-540-89639-5_54