Geodesic regression of image and shape data for improved modeling of 4D trajectories

James Fishbaugh, Marcel Prastawa, Guido Gerig, Stanley Durrleman

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

    Abstract

    A variety of regression schemes have been proposed on images or shapes, although available methods do not handle them jointly. In this paper, we present a framework for joint image and shape regression which incorporates images as well as anatomical shape information in a consistent manner. Evolution is described by a generative model that is the analog of linear regression, which is fully characterized by baseline images and shapes (intercept) and initial momenta vectors (slope). Further, our framework adopts a control point parameterization of deformations, where the dimensionality of the deformation is determined by the complexity of anatomical changes in time rather than the sampling of the image and/or the geometric data. We derive a gradient descent algorithm which simultaneously estimates baseline images and shapes, location of control points, and momenta. Experiments on real medical data demonstrate that our framework effectively combines image and shape information, resulting in improved modeling of 4D (3D space + time) trajectories.

    Original languageEnglish (US)
    Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages385-388
    Number of pages4
    ISBN (Print)9781467319591
    StatePublished - Jul 29 2014
    Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
    Duration: Apr 29 2014May 2 2014

    Other

    Other2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
    CountryChina
    CityBeijing
    Period4/29/145/2/14

    Fingerprint

    Momentum
    Trajectories
    Parameterization
    Linear regression
    Linear Models
    Joints
    Sampling
    Experiments

    ASJC Scopus subject areas

    • Biomedical Engineering
    • Radiology Nuclear Medicine and imaging

    Cite this

    Fishbaugh, J., Prastawa, M., Gerig, G., & Durrleman, S. (2014). Geodesic regression of image and shape data for improved modeling of 4D trajectories. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 385-388). [6867889] Institute of Electrical and Electronics Engineers Inc..

    Geodesic regression of image and shape data for improved modeling of 4D trajectories. / Fishbaugh, James; Prastawa, Marcel; Gerig, Guido; Durrleman, Stanley.

    2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 385-388 6867889.

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

    Fishbaugh, J, Prastawa, M, Gerig, G & Durrleman, S 2014, Geodesic regression of image and shape data for improved modeling of 4D trajectories. in 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014., 6867889, Institute of Electrical and Electronics Engineers Inc., pp. 385-388, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, China, 4/29/14.
    Fishbaugh J, Prastawa M, Gerig G, Durrleman S. Geodesic regression of image and shape data for improved modeling of 4D trajectories. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 385-388. 6867889
    Fishbaugh, James ; Prastawa, Marcel ; Gerig, Guido ; Durrleman, Stanley. / Geodesic regression of image and shape data for improved modeling of 4D trajectories. 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 385-388
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