Automatic Brain Tumor Segmentation by Subject Specific Modification of Atlas Priors

Marcel Prastawa, Elizabeth Bullitt, Nathan Moon, Koen Van Leemput, Guido Gerig

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Rationale and Objectives. Manual segmentation of brain tumors from magnetic resonance images is a challenging and time-consuming task. An automated system has been developed for brain tumor segmentation that will provide objective, reproducible segmentations that are close to the manual results. Additionally, the method segments white matter, grey matter, cerebrospinal fluid, and edema. The segmentation of pathology and healthy structures is crucial for surgical planning and intervention. Materials and Methods. The method performs the segmentation of a registered set of magnetic resonance images using an expectation-maximization scheme. The segmentation is guided by a spatial probabilistic atlas that contains expert prior knowledge about brain structures. This atlas is modified with the subject-specific brain tumor prior that is computed based on contrast enhancement. Results. Five cases with different types of tumors are selected for evaluation. The results obtained from the automatic segmentation program are compared with results from manual and semi-automated methods. The automated method yields results that have surface distances at roughly 1-4 mm compared with the manual results. Conclusion. The automated method can be applied to different types of tumors. Although its performance is below that of the semi-automated method, it has the advantage of requiring no user supervision.

    Original languageEnglish (US)
    Pages (from-to)1341-1348
    Number of pages8
    JournalAcademic Radiology
    Volume10
    Issue number12
    DOIs
    StatePublished - Dec 2003

    Keywords

    • Brain tumor segmentation
    • Expectation-maximization
    • Level-set evolution
    • Spatial atlas

    ASJC Scopus subject areas

    • Radiology Nuclear Medicine and imaging

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