Patient-specific conditional joint models of shape, image features and clinical indicators

Egger B, Schirmer MD, Dubost F, Nardin MJ, Rost NS, Golland P (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 11767 LNCS

Pages Range: 93-101

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Shenzhen CN

ISBN: 9783030322502

DOI: 10.1007/978-3-030-32251-9_11

Abstract

We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis. The key idea is to employ a copula model to separate the joint dependency structure from the marginal distributions of variables of interest. This separation provides flexibility on the assumptions made during the modeling process. The proposed method can handle binary, discrete, ordinal and continuous variables. We demonstrate a simple and efficient way to include binary, discrete and ordinal variables into the modeling. We build Bayesian conditional models based on observed partial clinical indicators, features or shape based on Gaussian processes capturing the dependency structure. We apply the proposed method on a stroke dataset to jointly model the shape of the lateral ventricles, the spatial distribution of the white matter hyperintensity associated with periventricular white matter disease, and clinical indicators. The proposed method yields interpretable joint models for data exploration and patient-specific statistical shape models for medical image analysis.

Authors with CRIS profile

Bernhard Egger

Involved external institutions

Harvard University US United States (USA) (US) Massachusetts Institute of Technology (MIT) US United States (USA) (US)

How to cite

APA:

Egger, B., Schirmer, M.D., Dubost, F., Nardin, M.J., Rost, N.S., & Golland, P. (2019). Patient-specific conditional joint models of shape, image features and clinical indicators. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 93-101). Shenzhen, CN: Springer Science and Business Media Deutschland GmbH.

MLA:

Egger, Bernhard, et al. "Patient-specific conditional joint models of shape, image features and clinical indicators." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 93-101.

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