Spatiotemporal Modeling for Image Time Series with Appearance Change: Application to Early Brain Development

James Fishbaugh, Martin Styner, Karen Grewen, John Gilmore, Guido Gerig

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

    Abstract

    There has been considerable research effort into image registration and regression, which address the problem of determining correspondence primarily through estimating models of structural change. There has been far less focus into methods which model both structural and intensity change. However, medical images often exhibit intensity changes over time. Of particular interest is MRI of the early developing brain, where such intensity change encodes rich information about development, such as rapidly increasing white matter intensity during the first years of life. In this paper, we develop a new spatiotemporal model which takes into account both structural and appearance changes jointly. This will not only lead to improved regression accuracy and data-matching in the presence of longitudinal intensity changes, but also facilitate the study of development by direct analysis of appearance change models. We propose to combine a diffeomorphic model of structural change with a Gompertz intensity model, which captures intensity trajectories with 3 intuitive parameters of asymptote, delay, and speed. We propose an optimization scheme which allows to control the balance between structural and intensity change via two data-matching terms. We show that Gompertz parameter maps show great promise to characterize regional patterns of development.

    Original languageEnglish (US)
    Title of host publicationMultimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy - 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Proceedings
    EditorsDajiang Zhu, Jingwen Yan, Heng Huang, Li Shen, Paul M. Thompson, Carl-Fredrik Westin, Xavier Pennec, Sarang Joshi, Mads Nielsen, Stefan Sommer, Tom Fletcher, Stanley Durrleman
    PublisherSpringer
    Pages174-185
    Number of pages12
    ISBN (Print)9783030332259
    DOIs
    StatePublished - Jan 1 2019
    Event4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
    Duration: Oct 17 2019Oct 17 2019

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11846 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer Assisted Intervention, MICCAI 2019
    CountryChina
    CityShenzhen
    Period10/17/1910/17/19

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    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

    Cite this

    Fishbaugh, J., Styner, M., Grewen, K., Gilmore, J., & Gerig, G. (2019). Spatiotemporal Modeling for Image Time Series with Appearance Change: Application to Early Brain Development. In D. Zhu, J. Yan, H. Huang, L. Shen, P. M. Thompson, C-F. Westin, X. Pennec, S. Joshi, M. Nielsen, S. Sommer, T. Fletcher, & S. Durrleman (Eds.), Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy - 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 174-185). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11846 LNCS). Springer. https://doi.org/10.1007/978-3-030-33226-6_19