User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Gee, Guido Gerig

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle segmentation are provided.

    Original languageEnglish (US)
    Pages (from-to)1116-1128
    Number of pages13
    JournalNeuroImage
    Volume31
    Issue number3
    DOIs
    StatePublished - Jul 1 2006

    Keywords

    • 3D active contour models
    • Anatomical objects
    • Caudate nucleus
    • Computational anatomy
    • Image segmentation
    • Open source software
    • Validation

    ASJC Scopus subject areas

    • Neurology
    • Cognitive Neuroscience

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