ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images

Paul A. Yushkevich, Yang Gao, Guido Gerig

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

    Abstract

    Obtaining quantitative measures from biomedical images often requires segmentation, i.e., finding and outlining the structures of interest. Multi-modality imaging datasets, in which multiple imaging measures are available at each spatial location, are increasingly common, particularly in MRI. In applications where fully automatic segmentation algorithms are unavailable or fail to perform at desired levels of accuracy, semi-automatic segmentation can be a time-saving alternative to manual segmentation, allowing the human expert to guide segmentation, while minimizing the effort expended by the expert on repetitive tasks that can be automated. However, few existing 3D image analysis tools support semi-automatic segmentation of multi-modality imaging data. This paper describes new extensions to the ITK-SNAP interactive image visualization and segmentation tool that support semi-automatic segmentation of multi-modality imaging datasets in a way that utilizes information from all available modalities simultaneously. The approach combines Random Forest classifiers, trained by the user by placing several brushstrokes in the image, with the active contour segmentation algorithm. The new multi-modality semi-automatic segmentation approach is evaluated in the context of high-grade glioblastoma segmentation.

    Original languageEnglish (US)
    Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3342-3345
    Number of pages4
    ISBN (Electronic)9781457702204
    DOIs
    StatePublished - Oct 13 2016
    Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
    Duration: Aug 16 2016Aug 20 2016

    Publication series

    NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
    Volume2016-October
    ISSN (Print)1557-170X

    Other

    Other38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
    CountryUnited States
    CityOrlando
    Period8/16/168/20/16

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    ASJC Scopus subject areas

    • Signal Processing
    • Biomedical Engineering
    • Computer Vision and Pattern Recognition
    • Health Informatics

    Cite this

    Yushkevich, P. A., Gao, Y., & Gerig, G. (2016). ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (pp. 3342-3345). [7591443] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2016.7591443