Generative Adversarial Registration for Improved Conditional Deformable Templates

Neel Dey, Mengwei Ren, Adrian V. Dalca, Guido Gerig

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

    Abstract

    Deformable templates are essential to large-scale medical image registration, segmentation, and population analysis. Current conventional and deep network-based methods for template construction use only regularized registration objectives and often yield templates with blurry and/or anatomically implausible appearance, confounding downstream biomedical interpretation. We reformulate deformable registration and conditional template estimation as an adversarial game wherein we encourage realism in the moved templates with a generative adversarial registration framework conditioned on flexible image covariates. The resulting templates exhibit significant gain in specificity to attributes such as age and disease, better fit underlying group-wise spatiotemporal trends, and achieve improved sharpness and centrality. These improvements enable more accurate population modeling with diverse covariates for standardized downstream analyses and easier anatomical delineation for structures of interest.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3909-3921
    Number of pages13
    ISBN (Electronic)9781665428125
    DOIs
    StatePublished - 2021
    Event18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada
    Duration: Oct 11 2021Oct 17 2021

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision
    ISSN (Print)1550-5499

    Conference

    Conference18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
    Country/TerritoryCanada
    CityVirtual, Online
    Period10/11/2110/17/21

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition

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