Facilitating manual segmentation of 3d datasets using contour and intensity guided interpolation

Sadhana Ravikumar, Laura Wisse, Yang Gao, Guido Gerig, Paul Yushkevich

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

    Abstract

    Manual segmentation of anatomical structures in 3D imaging datasets is a highly time-consuming process. This process can be sped up using interslice interpolation techniques, which require only a small subset of slices to be manually segmented. In this paper, we propose a two-step interpolation approach that utilizes a 'binary weighted averaging' algorithm to interpolate contour information, and the random forest framework to perform intensity-based label classification. We present the results of experiments performed in the context of hippocampal segmentations in ex vivo MRI scans. Compared to the random walker algorithm and morphology-based interpolation, the proposed method produces more accurate segmentations and smoother 3D reconstructions.

    Original languageEnglish (US)
    Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
    PublisherIEEE Computer Society
    Pages714-718
    Number of pages5
    ISBN (Electronic)9781538636411
    DOIs
    StatePublished - Apr 2019
    Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
    Duration: Apr 8 2019Apr 11 2019

    Publication series

    NameProceedings - International Symposium on Biomedical Imaging
    Volume2019-April
    ISSN (Print)1945-7928
    ISSN (Electronic)1945-8452

    Conference

    Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
    CountryItaly
    CityVenice
    Period4/8/194/11/19

    Keywords

    • 3D segmentation
    • Contour interpolation
    • Random forest

    ASJC Scopus subject areas

    • Biomedical Engineering
    • Radiology Nuclear Medicine and imaging

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