Diffeomorphic Metric Learning and Template Optimization for Registration-Based Predictive Models

Ayagoz Mussabayeva, Maxim Pisov, Anvar Kurmukov, Alexey Kroshnin, Yulia Denisova, Li Shen, Shan Cong, Lei Wang, Boris Gutman

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

Abstract

We present a method for metric optimization and template construction in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. The construction treats the Riemannian metric on the space of diffeomorphisms as a data-embedding kernel in the context of predictive modeling, here Kernel Logistic Regression (KLR). The task is then to optimize kernel parameters, including the LDDMM metric parameters as well as the registration template, resulting in a parameterized argminimum optimization. In practice, this leads to a group-wise registration problem with the goal of improving predictive performance, for example by focusing the metric and template on discriminating patient and control populations. We validate our algorithm using two discriminative problems on a synthetic data set as well as 3D subcortical shapes from the SchizConnect cohort. Though secondary to the template and kernel optimization, accuracy of schizophrenia classification is improved by LDDMM-KLR compared to linear and RBF-KLR.

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
Pages151-161
Number of pages11
ISBN (Print)9783030332259
DOIs
StatePublished - Jan 1 2019
Externally publishedYes
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

Keywords

  • Image registration
  • LDDMM
  • Machine learning
  • Metric learning
  • Subcortical shape

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Mussabayeva, A., Pisov, M., Kurmukov, A., Kroshnin, A., Denisova, Y., Shen, L., Cong, S., Wang, L., & Gutman, B. (2019). Diffeomorphic Metric Learning and Template Optimization for Registration-Based Predictive Models. 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. 151-161). (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_17