Estimation of smooth growth trajectories with controlled acceleration from time series shape data

James Fishbaugh, Stanley Durrleman, Guido Gerig

    Research output: Contribution to journalConference article

    Abstract

    Longitudinal shape analysis often relies on the estimation of a realistic continuous growth scenario from data sparsely distributed in time. In this paper, we propose a new type of growth model parameterized by acceleration, whereas standard methods typically control the velocity. This mimics the behavior of biological tissue as a mechanical system driven by external forces. The growth trajectories are estimated as smooth flows of deformations, which are twice differentiable. This differs from piecewise geodesic regression, for which the velocity may be discontinuous. We evaluate our approach on a set of anatomical structures of the same subject, scanned 16 times between 4 and 8 years of age. We show our acceleration based method estimates smooth growth, demonstrating improved regularity compared to piecewise geodesic regression. Leave-several-out experiments show that our method is robust to missing observations, as well as being less sensitive to noise, and is therefore more likely to capture the underlying biological growth.

    Original languageEnglish (US)
    Pages (from-to)401-408
    Number of pages8
    JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume6892 LNCS
    Issue numberPART 2
    DOIs
    StatePublished - 2011
    Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
    Duration: Sep 18 2011Sep 22 2011

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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