High-dimensional bayesian optimization of personalized cardiac model parameters via an embedded generative model

Jwala Dhamala, Sandesh Ghimire, John L. Sapp, B. Milan Horáček, Linwei Wang

Résultat de recherche: Conference contribution

11 Citations (Scopus)

Résumé

The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. However, these tissue properties are spatially varying across the underlying anatomical model, presenting a significance challenge of high-dimensional (HD) optimization at the presence of limited measurement data. A common solution to reduce the dimension of the parameter space is to explicitly partition the anatomical mesh, either into a fixed small number of segments or a multi-scale hierarchy. This anatomy-based reduction of parameter space presents a fundamental bottleneck to parameter estimation, resulting in solutions that are either too low in resolution to reflect tissue heterogeneity, or too high in dimension to be reliably estimated within feasible computation. In this paper, we present a novel concept that embeds a generative variational auto-encoder (VAE) into the objective function of Bayesian optimization, providing an implicit low-dimensional (LD) search space that represents the generative code of the HD spatially-varying tissue properties. In addition, the VAE-encoded knowledge about the generative code is further used to guide the exploration of the search space. The presented method is applied to estimating tissue excitability in a cardiac electrophysiological model. Synthetic and real-data experiments demonstrate its ability to improve the accuracy of parameter estimation with more than 10x gain in efficiency.

Langue d'origineEnglish
Titre de la publication principaleMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
ÉditeursGabor Fichtinger, Christos Davatzikos, Carlos Alberola-López, Alejandro F. Frangi, Julia A. Schnabel
Maison d'éditionSpringer Verlag
Pages499-507
Nombre de pages9
ISBN (imprimé)9783030009335
DOI
Statut de publicationPublished - 2018
Événement21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Durée: sept. 16 2018sept. 20 2018

Séries de publication

PrénomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11071 LNCS
ISSN (imprimé)0302-9743
ISSN (électronique)1611-3349

Conference

Conference21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Pays/TerritoireSpain
VilleGranada
Période9/16/189/20/18

Note bibliographique

Funding Information:
Acknowledgment. This work is supported by the National Science Foundation under CAREER Award ACI-1350374 and the National Institute of Heart, Lung, and Blood of the National Institutes of Health under Award R21Hl125998.

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

ASJC Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

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