Geodesic shape regression in the framework of currents

James Fishbaugh, Marcel Prastawa, Guido Gerig, Stanley Durrleman

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

    Abstract

    Shape regression is emerging as an important tool for the statistical analysis of time dependent shapes. In this paper, we develop a new generative model which describes shape change over time, by extending simple linear regression to the space of shapes represented as currents in the large deformation diffeomorphic metric mapping (LDDMM) framework. By analogy with linear regression, we estimate a baseline shape (intercept) and initial momenta (slope) which fully parameterize the geodesic shape evolution. This is in contrast to previous shape regression methods which assume the baseline shape is fixed. We further leverage a control point formulation, which provides a discrete and low dimensional parameterization of large diffeomorphic transformations. This flexible system decouples the parameterization of deformations from the specific shape representation, allowing the user to define the dimensionality of the deformation parameters. We present an optimization scheme that estimates the baseline shape, location of the control points, and initial momenta simultaneously via a single gradient descent algorithm. Finally, we demonstrate our proposed method on synthetic data as well as real anatomical shape complexes.

    Original languageEnglish (US)
    Title of host publicationInformation Processing in Medical Imaging - 23rd International Conference, IPMI 2013, Proceedings
    PublisherSpringer Verlag
    Pages718-729
    Number of pages12
    ISBN (Print)9783642388675
    DOIs
    StatePublished - 2013
    Event23rd International Conference on Information Processing in Medical Imaging, IPMI 2013 - Asilomar, CA, United States
    Duration: Jun 28 2013Jul 3 2013

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume7917 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other23rd International Conference on Information Processing in Medical Imaging, IPMI 2013
    CountryUnited States
    CityAsilomar, CA
    Period6/28/137/3/13

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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  • Cite this

    Fishbaugh, J., Prastawa, M., Gerig, G., & Durrleman, S. (2013). Geodesic shape regression in the framework of currents. In Information Processing in Medical Imaging - 23rd International Conference, IPMI 2013, Proceedings (pp. 718-729). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7917 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-38868-2_60